The University of Queensland

Database Systems for Advanced Applications. DASFAA 2023 International Workshops : BDMS 2023, BDQM 2023, GDMA 2023, BundleRS 2023, Tianjin, China, April 17-20, 2023, Proceedings

(2023). Database Systems for Advanced Applications. DASFAA 2023 International Workshops : BDMS 2023, BDQM 2023, GDMA 2023, BundleRS 2023, Tianjin, China, April 17-20, 2023, Proceedings. Cham: Springer Nature Switzerland. doi: 10.1007/978-3-031-35415-1

Spatio-temporal recommendation in social media

Yin, Hongzhi and Cui, Bin (2016). Spatio-temporal recommendation in social media. Singapore: Springer Singapore. doi: 10.1007/978-981-10-0748-4

Spatiotemporal recommendation with big geo-social networking data

Wang, Weiqing and Yin, Hongzhi (2019). Spatiotemporal recommendation with big geo-social networking data. Big data recommender systems - Volume 1: Algorithms, architectures, big data, security and trust. (pp. 193-224) edited by Osman Khalid, Samee U. Khan and Albert Y. Zomaya. Stevenage, United Kingdom: The Institution of Engineering and Technology. doi: 10.1049/pbpc035f_ch9

Spatiotemporal recommendation in geo-social networks

Yin, Hongzhi, Cui, Bin and Zhou, Xiaofang (2018). Spatiotemporal recommendation in geo-social networks. Encyclopedia of Social Network Analysis and Mining. (pp. 2930-2948) edited by Reda Alhajj and Jon Rokne. New York, NY, United States: Springer New York. doi: 10.1007/978-1-4939-7131-2_110177

Time interval-enhanced graph neural network for shared-account cross-domain sequential recommendation

Guo, Lei, Zhang, Jinyu, Tang, Li, Chen, Tong, Zhu, Lei and Yin, Hongzhi (2024). Time interval-enhanced graph neural network for shared-account cross-domain sequential recommendation. IEEE Transactions on Neural Networks and Learning Systems, 35 (3), 4002-4016. doi: 10.1109/tnnls.2022.3201533

MCRPL: A Pretrain, Prompt, and Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain Recommendation

Liu, Hao, Guo, Lei, Zhu, Lei, Jiang, Yongqiang, Gao, Min and Yin, Hongzhi (2024). MCRPL: A Pretrain, Prompt, and Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain Recommendation. ACM Transactions on Information Systems, 42 (4), 1-24. doi: 10.1145/3641860

XSimGCL: towards extremely simple graph contrastive learning for recommendation

Yu, Junliang, Xia, Xin, Chen, Tong, Cui, Lizhen, Hung, Nguyen Quoc Viet and Yin, Hongzhi (2024). XSimGCL: towards extremely simple graph contrastive learning for recommendation. IEEE Transactions on Knowledge and Data Engineering, 36 (2), 913-926. doi: 10.1109/tkde.2023.3288135

HiTSKT: A hierarchical transformer model for session-aware knowledge tracing

Ke, Fucai, Wang, Weiqing, Tan, Weicong, Du, Lan, Jin, Yuan, Huang, Yujin and Yin, Hongzhi (2024). HiTSKT: A hierarchical transformer model for session-aware knowledge tracing. Knowledge-Based Systems, 284 111300. doi: 10.1016/j.knosys.2023.111300

Portable graph-based rumour detection against multi-modal heterophily

Nguyen, Thanh Tam, Ren, Zhao, Nguyen, Thanh Toan, Jo, Jun, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2024). Portable graph-based rumour detection against multi-modal heterophily. Knowledge-Based Systems, 284 111310. doi: 10.1016/j.knosys.2023.111310

Heterogeneous decentralised machine unlearning with seed model distillation

Ye, Guanhua, Chen, Tong, Hung Nguyen, Quoc Viet and Yin, Hongzhi (2024). Heterogeneous decentralised machine unlearning with seed model distillation. CAAI Transactions on Intelligence Technology. doi: 10.1049/cit2.12281

Personalized Elastic Embedding Learning for On-Device Recommendation

Zheng, Ruiqi, Qu, Liang, Chen, Tong, Zheng, Kai, Shi, Yuhui and Yin, Hongzhi (2024). Personalized Elastic Embedding Learning for On-Device Recommendation. IEEE Transactions on Knowledge and Data Engineering, PP (99), 1-13. doi: 10.1109/tkde.2024.3361562

Self-supervised learning for recommender systems: a survey

Yu, Junliang, Yin, Hongzhi, Xia, Xin, Chen, Tong, Li, Jundong and Huang, Zi (2024). Self-supervised learning for recommender systems: a survey. IEEE Transactions on Knowledge and Data Engineering, 36 (1), 335-355. doi: 10.1109/tkde.2023.3282907

Isomorphic graph embedding for progressive maximal frequent subgraph mining

Nguyen, Thanh Toan, Nguyen, Thanh Tam, Nguyen, Thanh Hung, Yin, Hongzhi, Nguyen, Thanh Thi, Jo, Jun and Nguyen, Quoc Viet Hung (2023). Isomorphic graph embedding for progressive maximal frequent subgraph mining. ACM Transactions on Intelligent Systems and Technology, 15 (1) 9, 1-26. doi: 10.1145/3630635

Explicit Knowledge Graph Reasoning for Conversational Recommendation

Ren, Xuhui, Chen, Tong, Nguyen, Quoc Viet Hung, Cui, Lizhen, Huang, Zi and Yin, Hongzhi (2023). Explicit Knowledge Graph Reasoning for Conversational Recommendation. ACM Transactions on Intelligent Systems and Technology. doi: 10.1145/3637216

Heterogeneous collaborative learning for personalized healthcare analytics via messenger distillation

Ye, Guanhua, Chen, Tong, Li, Yawen, Cui, Lizhen, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2023). Heterogeneous collaborative learning for personalized healthcare analytics via messenger distillation. IEEE Journal of Biomedical and Health Informatics, 27 (11), 5249-5259. doi: 10.1109/jbhi.2023.3247463

Self-supervised hypergraph representation learning for sociological analysis

Sun, Xiangguo, Cheng, Hong, Liu, Bo, Li, Jia, Chen, Hongyang, Xu, Guandong and Yin, Hongzhi (2023). Self-supervised hypergraph representation learning for sociological analysis. IEEE Transactions on Knowledge and Data Engineering, 35 (11), 11860-11871. doi: 10.1109/tkde.2023.3235312

Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures

Yuan, Wei, Yuan, Shilong, Yang, Chaoqun, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2023). Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures. ACM Transactions on Information Systems, 42 (3), 1-26. doi: 10.1145/3630005

Efficient on-device session-based recommendation

Xia, Xin, Yu, Junliang, Wang, Qinyong, Yang, Chaoqun, Hung, Nguyen Quoc Viet and Yin, Hongzhi (2023). Efficient on-device session-based recommendation. ACM Transactions on Information Systems, 41 (4) 102, 1-24. doi: 10.1145/3580364

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Nguyen, Thanh Toan, Huynh, Thanh Trung, Weidlich, Matthias, Tho, Quan Thanh, Yin, Hongzhi, Aberer, Karl and Nguyen, Quoc Viet Hung (2023). Scalable maximal subgraph mining with backbone-preserving graph convolutions. Information Sciences, 644 119287, 119287. doi: 10.1016/j.ins.2023.119287

Structure learning via meta-hyperedge for dynamic rumor detection

Sun, Xiangguo, Yin, Hongzhi, Liu, Bo, Meng, Qing, Cao, Jiuxin, Zhou, Alexander and Chen, Hongxu (2023). Structure learning via meta-hyperedge for dynamic rumor detection. IEEE Transactions on Knowledge and Data Engineering, 35 (9), 9128-9139. doi: 10.1109/tkde.2022.3221438

Trustworthy recommendation and search: introduction to the special section - part 2

Yin, Hongzhi, Sun, Yizhou, Xu, Guandong and Kanoulas, Evangelos (2023). Trustworthy recommendation and search: introduction to the special section - part 2. ACM Transactions on Information Systems, 41 (4) 82, 1-6. doi: 10.1145/3604776

Reinforcement learning-enhanced shared-account cross-domain sequential recommendation

Guo, Lei, Zhang, Jinyu, Chen, Tong, Wang, Xinhua and Yin, Hongzhi (2023). Reinforcement learning-enhanced shared-account cross-domain sequential recommendation. IEEE Transactions on Knowledge and Data Engineering, 35 (7), 7397-7411. doi: 10.1109/tkde.2022.3185101

Spatial-temporal meta-path guided explainable crime prediction

Sun, Yuting, Chen, Tong and Yin, Hongzhi (2023). Spatial-temporal meta-path guided explainable crime prediction. World Wide Web, 26 (4), 2237-2263. doi: 10.1007/s11280-023-01137-3

Who are the best adopters? User selection model for free trial item promotion

Wang, Shiqi, Gao, Chongming, Gao, Min, Yu, Junliang, Wang, Zongwei and Yin, Hongzhi (2023). Who are the best adopters? User selection model for free trial item promotion. IEEE Transactions on Big Data, 9 (2), 746-757. doi: 10.1109/tbdata.2022.3205334

AutoML for deep recommender systems: a survey

Zheng, Ruiqi, Qu, Liang, Cui, Bin, Shi, Yuhui and Yin, Hongzhi (2023). AutoML for deep recommender systems: a survey. ACM Transactions on Information Systems, 41 (4) 101, 1-38. doi: 10.1145/3579355

Local feature-based mutual complexity for pixel-value-ordering reversible data hiding

Gao, Xinyi, Pan, Zhibin, Fan, Guojun, Zhang, Xiaoran and Yin, Hongzhi (2023). Local feature-based mutual complexity for pixel-value-ordering reversible data hiding. Signal Processing, 204 108833, 1-15. doi: 10.1016/j.sigpro.2022.108833

Time-Aware Dynamic Graph Embedding for Asynchronous Structural Evolution

Yang, Yu, Yin, Hongzhi, Cao, Jiannong, Chen, Tong, Nguyen, Quoc Viet Hung, Zhou, Xiaofang and Chen, Lei (2023). Time-Aware Dynamic Graph Embedding for Asynchronous Structural Evolution. IEEE Transactions on Knowledge and Data Engineering, 35 (9), 1-14. doi: 10.1109/tkde.2023.3246059

Decentralized collaborative learning framework for next POI recommendation

Long, Jing, Chen, Tong, Hung, Nguyen Quoc Viet and Yin, Hongzhi (2023). Decentralized collaborative learning framework for next POI recommendation. ACM Transactions on Information Systems, 41 (3) 66, 66:1-66:25. doi: 10.1145/3555374

ReFRS: Resource-efficient Federated Recommender System for dynamic and diversified user preferences

Imran, Mubashir, Yin, Hongzhi, Chen, Tong, Hung, Nguyen Quoc Viet, Zhou, Alexander and Zheng, Kai (2023). ReFRS: Resource-efficient Federated Recommender System for dynamic and diversified user preferences. ACM Transactions on Information Systems, 41 (3) 65, 65:1-65:30 . doi: 10.1145/3560486

Trustworthy Recommendation and Search: Introduction to the Special Issue - Part 1

Yin, Hongzhi, Sun, Yizhou, Xu, Guandong and Kanoulas, Evangelos (2023). Trustworthy Recommendation and Search: Introduction to the Special Issue - Part 1. ACM Transactions on Information Systems, 41 (3) 51, 1-5. doi: 10.1145/3579995

Deep MinCut: learning node embeddings by detecting communities

Duong, Chi Thang, Nguyen, Thanh Tam, Hoang, Trung-Dung, Yin, Hongzhi, Weidlich, Matthias and Nguyen, Quoc Viet Hung (2023). Deep MinCut: learning node embeddings by detecting communities. Pattern Recognition, 134 109126, 1-11. doi: 10.1016/j.patcog.2022.109126

Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning

Chen, Tong, Yin, Hongzhi, Ren, Jie, Huang, Zi, Zhang, Xiangliang and Wang, Hao (2023). Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning. Ieee Transactions On Knowledge and Data Engineering, 35 (2), 2103-2117. doi: 10.1109/TKDE.2021.3100529

A multi-strategy based pre-training method for cold-start recommendation

Hao, Bowen, Yin, Hongzhi, Zhang, Jing, Li, Cuiping and Chen, Hong (2023). A multi-strategy based pre-training method for cold-start recommendation. ACM Transactions on Information Systems, 41 (2) 31, 1-24. doi: 10.1145/3544107

Bayes-Enhanced Multi-View Attention Networks for Robust POI Recommendation

Xia, Jiangnan, Yang, Yu, Wang, Senzhang, Yin, Hongzhi, Cao, Jiannong and Yu, Philip S. (2023). Bayes-Enhanced Multi-View Attention Networks for Robust POI Recommendation. IEEE Transactions on Knowledge and Data Engineering, 1-14. doi: 10.1109/tkde.2023.3329673

Comprehensive privacy analysis on federated recommender system against attribute inference attacks

Zhang, Shijie, Yuan, Wei and Yin, Hongzhi (2023). Comprehensive privacy analysis on federated recommender system against attribute inference attacks. IEEE Transactions on Knowledge and Data Engineering, 36 (3), 987-999. doi: 10.1109/tkde.2023.3295601

Cost-effective synchrophasor data source authentication based on multiscale adaptive coupling correlation detrended analysis

Bai, Feifei, Cui, Yi, Yan, Ruifeng, Yin, Hongzhi, Chen, Tong, Dart, David and Yaghoobi, Jalil (2023). Cost-effective synchrophasor data source authentication based on multiscale adaptive coupling correlation detrended analysis. International Journal of Electrical Power and Energy Systems, 144 108606, 108606. doi: 10.1016/j.ijepes.2022.108606

Multi-hop knowledge graph reasoning in few-shot scenarios

Zheng, Shangfei, Chen, Wei, Wang, Weiqing, Zhao, Pengpeng, Yin, Hongzhi and Zhao, Lei (2023). Multi-hop knowledge graph reasoning in few-shot scenarios. IEEE Transactions on Knowledge and Data Engineering, 36 (4), 1713-1727. doi: 10.1109/tkde.2023.3304665

Proactive privacy-preserving learning for cross-modal retrieval

Zhang, Peng-Fei, Bai, Guangdong, Yin, Hongzhi and Huang, Zi (2023). Proactive privacy-preserving learning for cross-modal retrieval. ACM Transactions on Information Systems, 41 (2) ARTN 35, 1-23. doi: 10.1145/3545799

Special Issue of DASFAA 2023

Wang, Xin, Sapino, Maria Luisa, Han, Wook-Shin, Shao, Yingxiao and Yin, Hongzhi (2023). Special Issue of DASFAA 2023. Data Science and Engineering, 8 (3), 1-2. doi: 10.1007/s41019-023-00231-w

TinyAD: memory-efficient anomaly detection for time series data in industrial IoT

Sun, Yuting, Chen, Tong, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2023). TinyAD: memory-efficient anomaly detection for time series data in industrial IoT. IEEE Transactions on Industrial Informatics, 20 (1), 824-834. doi: 10.1109/tii.2023.3254668

Variational Counterfactual Prediction under Runtime Domain Corruption

Wen, Hechuan, Chen, Tong, Chai, Li Kheng, Sadiq, Shazia, Gao, Junbin and Yin, Hongzhi (2023). Variational Counterfactual Prediction under Runtime Domain Corruption. IEEE Transactions on Knowledge and Data Engineering, 1-14. doi: 10.1109/tkde.2023.3321893

Self-supervised graph learning for occasional group recommendation

Hao, Bowen, Yin, Hongzhi, Li, Cuiping and Chen, Hong (2022). Self-supervised graph learning for occasional group recommendation. International Journal of Intelligent Systems, 37 (12), 10880-10902. doi: 10.1002/int.23011

Model-agnostic and diverse explanations for streaming rumour graphs

Nguyen, Thanh Tam, Phan, Thanh Cong, Nguyen, Minh Hieu, Weidlich, Matthias, Yin, Hongzhi, Jo, Jun and Nguyen, Quoc Viet Hung (2022). Model-agnostic and diverse explanations for streaming rumour graphs. Knowledge-Based Systems, 253 109438, 1-15. doi: 10.1016/j.knosys.2022.109438

Multiscale adaptive multifractal detrended fluctuation analysis-based source identification of synchrophasor data

Cui, Yi, Bai, Feifei, Yin, Hongzhi, Chen, Tong, Dart, David, Zillmann, Matthew and Ko, Ryan K. L. (2022). Multiscale adaptive multifractal detrended fluctuation analysis-based source identification of synchrophasor data. IEEE Transactions on Smart Grid, 13 (6), 1-4. doi: 10.1109/tsg.2022.3207066

Detecting rumours with latency guarantees using massive streaming data

Nguyen, Thanh Tam, Huynh, Thanh Trung, Yin, Hongzhi, Weidlich, Matthias, Nguyen, Thanh Thi, Mai, Thai Son and Nguyen, Quoc Viet Hung (2022). Detecting rumours with latency guarantees using massive streaming data. VLDB Journal, 32 (2), 1-19. doi: 10.1007/s00778-022-00750-4

Multi-graph heterogeneous interaction fusion for social recommendation

Zhang, Chengyuan, Wang, Yang, Zhu, Lei, Song, Jiayu and Yin, Hongzhi (2022). Multi-graph heterogeneous interaction fusion for social recommendation. ACM Transactions on Information Systems, 40 (2) 3466641, 1-26. doi: 10.1145/3466641

Sequential-knowledge-aware next POI recommendation: a meta-learning approach

Cui, Yue, Sun, Hao, Zhao, Yan, Yin, Hongzhi and Zheng, Kai (2022). Sequential-knowledge-aware next POI recommendation: a meta-learning approach. ACM Transactions on Information Systems, 40 (2) 3460198, 1-22. doi: 10.1145/3460198

Exploiting positional information for session-based recommendation

Qiu, Ruihong, Huang, Zi, Chen, Tong and Yin, Hongzhi (2022). Exploiting positional information for session-based recommendation. ACM Transactions on Information Systems, 40 (2) 3473339, 1-24. doi: 10.1145/3473339

HFUL: a hybrid framework for user account linkage across location-aware social networks

Chen, Wei, Wang, Weiqing, Yin, Hongzhi, Zhao, Lei and Zhou, Xiaofang (2022). HFUL: a hybrid framework for user account linkage across location-aware social networks. VLDB Journal, 32 (1), 1-22. doi: 10.1007/s00778-022-00730-8

An integrated first principal and deep learning approach for modeling nitrous oxide emissions from wastewater treatment plants

Li, Kaili, Duan, Haoran, Liu, Linfeng, Qiu, Ruihong, van den Akker, Ben, Ni, Bing-Jie, Chen, Tong, Yin, Hongzhi, Yuan, Zhiguo and Ye, Liu (2022). An integrated first principal and deep learning approach for modeling nitrous oxide emissions from wastewater treatment plants. Environmental Science and Technology, 56 (4) acs.est.1c05020, 2816-2826. doi: 10.1021/acs.est.1c05020

Introduction to the Special Issue on Intelligent Trajectory Analytics: Part I

Zheng, Kai, Li, Yong, Shahabi, Cyrus and Yin, Hongzhi (2022). Introduction to the Special Issue on Intelligent Trajectory Analytics: Part I. ACM Transactions on Intelligent Systems and Technology, 13 (1) 1, 1-2. doi: 10.1145/3495230

Passenger mobility prediction via representation learning for dynamic directed and weighted graphs

Wang, Yuandong, Yin, Hongzhi, Chen, Tong, Liu, Chunyang, Wang, Ben, Wo, Tianyu and Xu, Jie (2022). Passenger mobility prediction via representation learning for dynamic directed and weighted graphs. ACM Transactions on Intelligent Systems and Technology, 13 (1) 2, 1-25. doi: 10.1145/3446344

Hierarchical hyperedge embedding-based representation learning for group recommendation

Guo, Lei, Yin, Hongzhi, Chen, Tong, Zhang, Xiangliang and Zheng, Kai (2022). Hierarchical hyperedge embedding-based representation learning for group recommendation. ACM Transactions on Information Systems, 40 (1) 3, 1-27. doi: 10.1145/3457949

DeHIN: a decentralized framework for embedding large-scale heterogeneous information networks

Imran, Mubashir, Yin, Hongzhi, Chen, Tong, Huang, Zi and Zheng, Kai (2022). DeHIN: a decentralized framework for embedding large-scale heterogeneous information networks. IEEE Transactions on Knowledge and Data Engineering, PP (99), 1-1. doi: 10.1109/TKDE.2022.3141951

LECF: recommendation via learnable edge collaborative filtering

Xiao, Shitao, Shao, Yingxia, Li, Yawen, Yin, Hongzhi, Shen, Yanyan and Cui, Bin (2022). LECF: recommendation via learnable edge collaborative filtering. Science China Information Sciences, 65 (1) 112101. doi: 10.1007/s11432-020-3274-6

Learning holistic interactions in LBSNs with high-order, dynamic, and multi-role contexts

Huynh, Thanh Trung, Tong, Vinh Van, Nguyen, Thanh Tam, Jo, Jun, Yin, Hongzhi and Nguyen, Quoc Viet Hung (2022). Learning holistic interactions in LBSNs with high-order, dynamic, and multi-role contexts. IEEE Transactions on Knowledge & Data Engineering, PP (99), 1-1. doi: 10.1109/TKDE.2022.3150792

Personalized on-device e-health analytics with decentralized block coordinate descent

Ye, Guanhua, Yin, Hongzhi, Chen, Tong, Xu, Miao, Nguyen, Quoc Viet Hung and Song, Jiangning (2022). Personalized on-device e-health analytics with decentralized block coordinate descent. IEEE Journal of Biomedical and Health Informatics, 26 (6), 1-1. doi: 10.1109/JBHI.2022.3140455

Preface - Special Issue on Misinformation on the Web

Aberer, Karl, Katakis, Ioannis, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2022). Preface - Special Issue on Misinformation on the Web. Information Systems, 103 101867, 101867. doi: 10.1016/j.is.2021.101867

Interpretable signed link prediction with signed infomax hyperbolic graph

Luo, Yadan, Huang, Zi, Chen, Hongxu, Yang, Yang, Yin, Hongzhi and Baktashmotlagh, Mahsa (2021). Interpretable signed link prediction with signed infomax hyperbolic graph. IEEE Transactions on Knowledge and Data Engineering, PP (99), 1-1. doi: 10.1109/TKDE.2021.3139035

Scalable robust graph embedding with Spark

Duong, Chi Thang, Hoang, Trung Dung, Yin, Hongzhi, Weidlich, Matthias, Nguyen, Quoc Viet Hung and Aberer, Karl (2021). Scalable robust graph embedding with Spark. Proceedings of the VLDB Endowment, 15 (4), 914-922. doi: 10.14778/3503585.3503599

Towards Revenue Maximization with Popular and Profitable Products

Gan, Wensheng, Chen, Guoting, Yin, Hongzhi, Fournier-Viger, Philippe, Chen, Chien-Ming and Yu, Philip S. (2021). Towards Revenue Maximization with Popular and Profitable Products. ACM/IMS Transactions on Data Science, 2 (4), 1-21. doi: 10.1145/3488058

Quaternion factorization machines: a lightweight solution to intricate feature interaction modeling

Chen, Tong, Yin, Hongzhi, Zhang, Xiangliang, Huang, Zi, Wang, Yang and Wang, Meng (2021). Quaternion factorization machines: a lightweight solution to intricate feature interaction modeling. IEEE Transactions on Neural Networks and Learning Systems, PP (99), 1-14. doi: 10.1109/TNNLS.2021.3118706

Fast-adapting and privacy-preserving federated recommender system

Wang, Qinyong, Yin, Hongzhi, Chen, Tong, Yu, Junliang, Zhou, Alexander and Zhang, Xiangliang (2021). Fast-adapting and privacy-preserving federated recommender system. The VLDB Journal, 31 (5), 877-896. doi: 10.1007/s00778-021-00700-6

A block-based generative model for attributed network embedding

Liu, Xueyan, Yang, Bo, Song, Wenzhuo, Musial, Katarzyna, Zuo, Wanli, Chen, Hongxu and Yin, Hongzhi (2021). A block-based generative model for attributed network embedding. World Wide Web, 24 (5), 1439-1464. doi: 10.1007/s11280-021-00918-y

Network alignment with holistic embeddings

Huynh, Thanh Trung, Duong, Chi Thang, Nguyen, Tam Thanh, Tong, Vinh Van, Sattar, Abdul, Yin, Hongzhi and Nguyen, Quoc Viet Hung (2021). Network alignment with holistic embeddings. IEEE Transactions on Knowledge and Data Engineering, PP (99), 1-1. doi: 10.1109/TKDE.2021.3101840

Utility mining across multi-dimensional sequences

Gan, Wensheng, Lin, Jerry Chun-Wei, Zhang, Jiexiong, Yin, Hongzhi, Fournier-Viger, Philippe, Chao, Han-Chieh and Yu, Philip S. (2021). Utility mining across multi-dimensional sequences. ACM Transactions on Knowledge Discovery from Data, 15 (5) 3446938, 1-24. doi: 10.1145/3446938

An integrated model based on deep multimodal and rank learning for point-of-interest recommendation

Liao, Jianxin, Liu, Tongcun, Yin, Hongzhi, Chen, Tong, Wang, Jingyu and Wang, Yulong (2021). An integrated model based on deep multimodal and rank learning for point-of-interest recommendation. World Wide Web, 24 (2), 631-655. doi: 10.1007/s11280-021-00865-8

Disease prediction via graph neural networks

Sun, Zhenchao, Yin, Hongzhi, Chen, Hongxu, Chen, Tong, Cui, Lizhen and Yang, Fan (2021). Disease prediction via graph neural networks. IEEE Journal of Biomedical and Health Informatics, 25 (3) 9122573, 818-826. doi: 10.1109/JBHI.2020.3004143

Efficient and effective multi-modal queries through heterogeneous network embedding

Duong, Chi Thang, Nguyen, Tam Thanh, Yin, Hongzhi, Weidlich, Matthias, Mai, Son, Aberer, Karl and Nguyen, Quoc Viet Hung (2021). Efficient and effective multi-modal queries through heterogeneous network embedding. IEEE Transactions on Knowledge and Data Engineering, 34 (11), 1-1. doi: 10.1109/TKDE.2021.3052871

Efficient streaming subgraph isomorphism with graph neural networks

Duong, Chi Thang, Hoang, Trung Dung, Yin, Hongzhi, Weidlich, Matthias, Nguyen, Quoc Viet Hung and Aberer, Karl (2021). Efficient streaming subgraph isomorphism with graph neural networks. Proceedings of the VLDB Endowment, 14 (5), 730-742. doi: 10.14778/3446095.3446097

FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection

Ye, Guanhua, Yin, Hongzhi, Chen, Tong, Chen, Hongxu, Cui, Lizhen and Zhang, Xiangliang (2021). FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection. IEEE Journal of Biomedical and Health Informatics, 25 (8) 9320528, 2848-2856. doi: 10.1109/JBHI.2021.3050113

Multifractal characterization of distribution synchrophasors for cybersecurity defense of smart grids

Cui, Yi, Bai, Feifei, Yan, Ruifeng, Saha, Tapan, Mosadeghy, Mehdi, Yin, Hongzhi, Ko, Ryan K. L. and Liu, Yilu (2021). Multifractal characterization of distribution synchrophasors for cybersecurity defense of smart grids. IEEE Transactions on Smart Grid, 13 (2), 1658-1661. doi: 10.1109/tsg.2021.3132536

Reinforced KGs reasoning for explainable sequential recommendation

Cui, Zhihong, Chen, Hongxu, Cui, Lizhen, Liu, Shijun, Liu, Xueyan, Xu, Guandong and Yin, Hongzhi (2021). Reinforced KGs reasoning for explainable sequential recommendation. World Wide Web, 25 (2), 631-654. doi: 10.1007/s11280-021-00902-6

Secure your ride: real-time matching success rate prediction for passenger-driver pairs

Wang, Yuandong, Yin, Hongzhi, Wu, Lian, Chen, Tong and Liu, Chunyang (2021). Secure your ride: real-time matching success rate prediction for passenger-driver pairs. IEEE Transactions on Knowledge and Data Engineering, PP (99), 1-14. doi: 10.1109/tkde.2021.3112739

Uniting heterogeneity, inductiveness, and efficiency for graph representation learning

Chen, Tong, Yin, Hongzhi, Ren, Jie, Huang, Zi, Zhang, Xiangliang and Wang, Hao (2021). Uniting heterogeneity, inductiveness, and efficiency for graph representation learning. IEEE Transactions on Knowledge and Data Engineering, PP (99), 1-1. doi: 10.1109/TKDE.2021.3100529

Enhanced factorization machine via neural pairwise ranking and attention networks

Yu, Yonghong, Jiao, Lihong, Zhou, Ningning, Zhang, Li and Yin, Hongzhi (2020). Enhanced factorization machine via neural pairwise ranking and attention networks. Pattern Recognition Letters, 140, 348-357. doi: 10.1016/j.patrec.2020.11.010

Entity alignment for knowledge graphs with multi-order convolutional networks

Nguyen, Tam Thanh, Huynh, Thanh Trung, Yin, Hongzhi, Tong, Vinh Van, Sakong, Darnbi, Zheng, Bolong and Nguyen, Quoc Viet Hung (2020). Entity alignment for knowledge graphs with multi-order convolutional networks. IEEE Transactions on Knowledge and Data Engineering, 34 (9), 1-1. doi: 10.1109/TKDE.2020.3038654

Enhance social recommendation with adversarial graph convolutional networks

Yu, Junliang, Yin, Hongzhi, Li, Jundong, Gao, Min, Huang, Zi and Cui, Lizhen (2020). Enhance social recommendation with adversarial graph convolutional networks. IEEE Transactions on Knowledge and Data Engineering, 34 (8), 1-1. doi: 10.1109/tkde.2020.3033673

CRSAL: conversational recommender systems with adversarial learning

Ren, Xuhui, Yin, Hongzhi, Chen, Tong, Wang, Hao, Hung, Nguyen Quoc Viet, Huang, Zi and Zhang, Xiangliang (2020). CRSAL: conversational recommender systems with adversarial learning. ACM Transactions on Information Systems, 38 (4) 3394592, 1-40. doi: 10.1145/3394592

Semantic trajectory representation and retrieval via hierarchical embedding

Gao, Chongming, Zhang, Zhong, Huang, Chen, Yin, Hongzhi, Yang, Qinli and Shao, Junming (2020). Semantic trajectory representation and retrieval via hierarchical embedding. Information Sciences, 538, 176-192. doi: 10.1016/j.ins.2020.05.107

Deep pairwise hashing for cold-start recommendation

Zhang, Yan, Tsang, Ivor, Yin, Hongzhi, Yang, Guowu, Lian, Defu and Li, Jingjing (2020). Deep pairwise hashing for cold-start recommendation. IEEE Transactions on Knowledge and Data Engineering, 34 (7), 1-1. doi: 10.1109/tkde.2020.3024022

Overcoming data sparsity in group recommendation

Yin, Hongzhi, Wang, Qinyong, Zheng, Kai, Li, Zhixu and Zhou, Xiaofang (2020). Overcoming data sparsity in group recommendation. IEEE Transactions on Knowledge and Data Engineering, 34 (7), 1-1. doi: 10.1109/tkde.2020.3023787

TEAGS: time-aware text embedding approach to generate subgraphs

Hosseini, Saeid, Najafipour, Saeed, Cheung, Ngai-Man, Yin, Hongzhi, Kangavari, Mohammad Reza and Zhou, Xiaofang (2020). TEAGS: time-aware text embedding approach to generate subgraphs. Data Mining and Knowledge Discovery, 34 (4), 1136-1174. doi: 10.1007/s10618-020-00688-7

User account linkage across multiple platforms with location data

Chen, Wei, Wang, Weiqing, Yin, Hongzhi, Fang, Jun-Hua and Zhao, Lei (2020). User account linkage across multiple platforms with location data. Journal of Computer Science and Technology, 35 (4), 751-768. doi: 10.1007/s11390-020-0250-7

Extracting representative user subset of social networks towards user characteristics and topological features

Zhou, Yiming, Han, Yuehui, Liu, An, Li, Zhixu, Yin, Hongzhi, Chen, Wei and Zhao, Lei (2020). Extracting representative user subset of social networks towards user characteristics and topological features. World Wide Web, 23 (5), 2903-2931. doi: 10.1007/s11280-020-00828-5

Exploiting cross-session information for session-based recommendation with graph neural networks

Qiu, Ruihong, Huang, Zi, Li, Jingjing and Yin, Hongzhi (2020). Exploiting cross-session information for session-based recommendation with graph neural networks. ACM Transactions on Information Systems, 38 (3) 22, 1-23. doi: 10.1145/3382764

Social boosted recommendation with folded bipartite network embedding

Chen, Hongxu, Yin, Hongzhi, Chen, Tong, Wang, Weiqing, Li, Xue and Hu, Xia (2020). Social boosted recommendation with folded bipartite network embedding. IEEE Transactions on Knowledge and Data Engineering, 34 (2), 914-926. doi: 10.1109/tkde.2020.2982878

Group-based recurrent neural networks for POI recommendation

Li, Guohui, Chen, Qi, Zheng, Bolong, Yin, Hongzhi, Nguyen, Quoc Viet Hung and Zhou, Xiaofang (2020). Group-based recurrent neural networks for POI recommendation. ACM/IMS Transactions on Data Science, 1 (1) 3, 1-18. doi: 10.1145/3343037

Cluster query: a new query pattern on temporal knowledge graph

Huang, Jinjing, Chen, Wei, Liu, An, Wang, Weiqing, Yin, Hongzhi and Zhao, Lei (2020). Cluster query: a new query pattern on temporal knowledge graph. World Wide Web, 23 (2), 755-779. doi: 10.1007/s11280-019-00754-1

Local variational feature-based similarity models for recommending top-N new items

Chen, Yifan, Wang, Yang, Zhao, Xiang, Yin, Hongzhi, Markov, Ilya and De Rijke, Maarten (2020). Local variational feature-based similarity models for recommending top-N new items. ACM Transactions on Information Systems, 38 (2) 12, 1-33. doi: 10.1145/3372154

SGPM: a privacy protected approach of time-constrained graph pattern matching in cloud

Huang, Jinjing, Chen, Wei, Li, Zhixu, Zhao, Pengpeng, Wang, Weiqing, Yin, Hongzhi and Zhao, Lei (2020). SGPM: a privacy protected approach of time-constrained graph pattern matching in cloud. World Wide Web-Internet and Web Information Systems, 23 (1), 519-547. doi: 10.1007/s11280-020-00784-0

Few-shot deep adversarial learning for video-based person re-identification

Wu, Lin, Wang, Yang, Yin, Hongzhi, Wang, Meng and Shao, Ling (2020). Few-shot deep adversarial learning for video-based person re-identification. IEEE Transactions on Image Processing, 29 8839731, 1233-1245. doi: 10.1109/tip.2019.2940684

Preference-aware task assignment in spatial crowdsourcing: from individuals to groups

Zhao, Yan, Zheng, Kai, Yin, Hongzhi, Liu, Guanfeng, Fang, Junhua and Zhou, Xiaofang (2020). Preference-aware task assignment in spatial crowdsourcing: from individuals to groups. IEEE Transactions on Knowledge and Data Engineering, 34 (7), 1-1. doi: 10.1109/tkde.2020.3021028

An efficient framework for multiple subgraph pattern matching models

Gao, Jiu-Ru, Chen, Wei, Xu, Jia-Jie, Liu, An, Li, Zhi-Xu, Yin, Hongzhi and Zhao, Lei (2019). An efficient framework for multiple subgraph pattern matching models. Journal of Computer Science and Technology, 34 (6), 1185-1202. doi: 10.1007/s11390-019-1969-x

Online sales prediction via trend alignment-based multitask recurrent neural networks

Chen, Tong, Yin, Hongzhi, Chen, Hongxu, Wang, Hao, Zhou, Xiaofang and Li, Xue (2019). Online sales prediction via trend alignment-based multitask recurrent neural networks. Knowledge and Information Systems, 62 (6), 2139-2167. doi: 10.1007/s10115-019-01404-8

Group-level personality detection based on text generated networks

Sun, Xiangguo, Liu, Bo, Meng, Qing, Cao, Jiuxin, Luo, Junzhou and Yin, Hongzhi (2019). Group-level personality detection based on text generated networks. World Wide Web, 23 (3), 1887-1906. doi: 10.1007/s11280-019-00729-2

Semi-supervised clustering with deep metric learning and graph embedding

Li, Xiaocui, Yin, Hongzhi, Zhou, Ke and Zhou, Xiaofang (2019). Semi-supervised clustering with deep metric learning and graph embedding. World Wide Web, 23 (2), 781-798. doi: 10.1007/s11280-019-00723-8

Efficient user guidance for validating participatory sensing data

Cong, Phan Thanh, Tam, Nguyen Thanh, Yin, Hongzhi, Zheng, Bolong, Stantic, Bela and Hung, Nguyen Quoc Viet (2019). Efficient user guidance for validating participatory sensing data. ACM Transactions on Intelligent Systems and Technology, 10 (4) 37, 1-30. doi: 10.1145/3326164

MCP: a multi-component learning machine to predict protein secondary structure

Khalatbari, Leila, Kangavari, M. R., Hosseini, Saeid, Yin, Hongzhi and Cheung, Ngai-Man (2019). MCP: a multi-component learning machine to predict protein secondary structure. Computers in Biology and Medicine, 110, 144-155. doi: 10.1016/j.compbiomed.2019.04.040

Leveraging multi-aspect time-related influence in location recommendation

Hosseini, Saeid, Yin, Hongzhi, Zhou, Xiaofang, Sadiq, Shazia, Kangavari, Mohammad Reza and Cheung, Ngai-Man (2019). Leveraging multi-aspect time-related influence in location recommendation. World Wide Web, 22 (3), 1001-1028. doi: 10.1007/s11280-018-0573-2

Spatiotemporal representation learning for translation-based POI recommendation

Qian, Tieyun, Liu, Bei, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2019). Spatiotemporal representation learning for translation-based POI recommendation. ACM Transactions on Information Systems, 37 (2) 18, 1-24. doi: 10.1145/3295499

Spatiotemporal recommendation with big geo-social networking data

Wang, Weiqing and Yin, Hongzhi (2019). Spatiotemporal recommendation with big geo-social networking data. IET Professional Applications of Computing Series, 35, 193-224.

Personalized video recommendation using rich contents from videos

Du, Xingzhong, Yin, Hongzhi, Chen, Ling, Wang, Yang, Yang, Yi and Zhou, Xiaofang (2018). Personalized video recommendation using rich contents from videos. IEEE Transactions on Knowledge and Data Engineering, 32 (3) 8567986, 1-1. doi: 10.1109/TKDE.2018.2885520

TPM: a temporal personalized model for spatial item recommendation

Wang, Weiqing, Yin, Hongzhi, Du, Xingzhong, Nguyen, Quoc Viet Hung and Zhou, Xiaofang (2018). TPM: a temporal personalized model for spatial item recommendation. ACM Transactions on Intelligent Systems and Technology, 9 (6) a61, 1-25. doi: 10.1145/3230706

Mobi-SAGE-RS: A sparse additive generative model-based mobile application recommender system

Yin, Hongzhi, Wang, Weiqing, Chen, Liang, Du, Xingzhong, Hung Nguyen, Quoc Viet and Huang, Zi (2018). Mobi-SAGE-RS: A sparse additive generative model-based mobile application recommender system. Knowledge-Based Systems, 157, 68-80. doi: 10.1016/j.knosys.2018.05.028

Preface

Zhou, Xiaofang and Yin, Hongzhi (2018). Preface. Journal of Computer Science and Technology, 33 (4), 621-624. doi: 10.1007/s11390-018-1844-1

A deep dive into user display names across social networks

Li, Yongjun, Peng, You, Zhang, Zhen, Wu, Mingjie, Xu, Quanqing and Yin, Hongzhi (2018). A deep dive into user display names across social networks. Information Sciences, 447, 186-204. doi: 10.1016/j.ins.2018.02.072

Matching user accounts based on user generated content across social networks

Li, Yongjun, Zhang, Zhen, Peng, You, Yin, Hongzhi and Xu, Quanqing (2018). Matching user accounts based on user generated content across social networks. Future Generation Computer Systems, 83, 104-115. doi: 10.1016/j.future.2018.01.041

Layered convolutional dictionary learning for sparse coding itemsets

Mansha, Sameen, Lam, Hoang Thanh, Yin, Hongzhi, Kamiran, Faisal and Ali, Mohsen (2018). Layered convolutional dictionary learning for sparse coding itemsets. World Wide Web, 22 (5), 1-15. doi: 10.1007/s11280-018-0565-2

Matching user accounts across social networks based on username and display name

Li, Yongjun, Peng, You, Zhang, Zhen, Yin, Hongzhi and Xu, Quanqing (2018). Matching user accounts across social networks based on username and display name. World Wide Web, 22 (3), 1-23. doi: 10.1007/s11280-018-0571-4

User identity linkage across social networks via linked heterogeneous network embedding

Wang, Yaqing, Feng, Chunyan, Chen, Ling, Yin, Hongzhi, Guo, Caili and Chu, Yunfei (2018). User identity linkage across social networks via linked heterogeneous network embedding. World Wide Web, 22 (6), 1-22. doi: 10.1007/s11280-018-0572-3

Computing crowd consensus with partial agreement

Viet Hung, Nguyen Quoc, Viet, Huynh Huu, Tam, Nguyen Thanh, Weidlich, Matthias, Yin, Hongzhi and Zhou, Xiaofang (2018). Computing crowd consensus with partial agreement. IEEE Transactions on Knowledge and Data Engineering, 30 (1), 1-14. doi: 10.1109/TKDE.2017.2750683

Spatial-aware hierarchical collaborative deep learning for POI recommendation

Yin, Hongzhi, Wang, Weiqing, Wang, Hao, Chen, Ling and Zhou, Xiaofang (2017). Spatial-aware hierarchical collaborative deep learning for POI recommendation. Ieee Transactions On Knowledge and Data Engineering, 29 (11) 8013107, 2537-2551. doi: 10.1109/TKDE.2017.2741484

Exploiting detected visual objects for frame-level video filtering

Du, Xingzhong, Yin, Hongzhi, Huang, Zi, Yang, Yi and Zhou, Xiaofang (2017). Exploiting detected visual objects for frame-level video filtering. World Wide Web, 21 (5), 1-26. doi: 10.1007/s11280-017-0505-6

Answer validation for generic crowdsourcing tasks with minimal efforts

Hung, Nguyen Quoc Viet, Thang, Duong Chi, Tam, Nguyen Thanh, Weidlich, Matthias, Aberer, Karl, Yin, Hongzhi and Zhou, Xiaofang (2017). Answer validation for generic crowdsourcing tasks with minimal efforts. Vldb Journal, 26 (6), 855-880. doi: 10.1007/s00778-017-0484-3

Argument discovery via crowdsourcing

Nguyen, Quoc Viet Hung, Duong, Chi Thang, Nguyen, Thanh Tam, Weidlich, Matthias, Aberer, Karl, Yin, Hongzhi and Zhou, Xiaofang (2017). Argument discovery via crowdsourcing. VLDB Journal, 26 (4), 511-535. doi: 10.1007/s00778-017-0462-9

ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation

Wang, Weiqing , Yin, Hongzhi , Chen, Ling , Sun, Yizhou , Sadiq, Shazia and Zhou, Xiaofang (2017). ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation. ACM Transactions on Intelligent Systems and Technology, 8 (3) 48, 48.1-48.25. doi: 10.1145/3011019

A fast sketch-based approach of top-k closeness centrality search on large networks

Shao, Y.-X., Cui, B., Ma, L. and Yin, H.-Z. (2016). A fast sketch-based approach of top-k closeness centrality search on large networks. Jisuanji Xuebao/Chinese Journal of Computers, 39 (10), 1965-1978. doi: 10.11897/SP.J.1016.2016.01965

A spatial-temporal topic model for the semantic annotation of POIs in LBSNs

He, Tieke, Yin, Hongzhi, Chen, Zhenyu, Zhou, Xiaofang, Sadiq, Shazia and Luo, Bin (2016). A spatial-temporal topic model for the semantic annotation of POIs in LBSNs. ACM Transactions on Intelligent Systems and Technology, 8 (1) 12, 1-24. doi: 10.1145/2905373

Adapting to user interest drift for POI recommendation

Yin, Hongzhi, Zhou, Xiaofang, Cui, Bin, Wang, Hao, Zheng, Kai and Nguyen, Quoc Viet Hung (2016). Adapting to user interest drift for POI recommendation. IEEE Transactions on Knowledge and Data Engineering, 28 (10) 7491346, 2566-2581. doi: 10.1109/TKDE.2016.2580511

Joint modeling of user check-in behaviors for real-time point-of-interest recommendation

Yin, Hongzhi, Cui, Bin, Zhou, Xiaofang, Wang, Weiqing, Huang, Zi and Sadiq, Shazia (2016). Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Transactions on Information Systems, 35 (2) 2873055, 1-44. doi: 10.1145/2873055

Modeling location-based user rating profiles for personalized recommendation

Yin, Hongzhi, Cui, Bin, Chen, Ling, Hu, Zhiting and Zhang, Chengqi (2015). Modeling location-based user rating profiles for personalized recommendation. ACM Transactions on Knowledge Discovery from Data, 9 (3) 19, 19:1-19:41. doi: 10.1145/2663356

Dynamic user modeling in social media systems

Yin, Hongzhi, Cui, Bin, Chen, Ling, Hu, Zhiting and Zhou, Xiaofang (2015). Dynamic user modeling in social media systems. ACM Transactions on Information Systems, 33 (3) 10, 10:1-10:44. doi: 10.1145/2699670

Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion

Xie, Yiran, Yin, Hongzhi, Cui, Bin, Yao, Junjie and Xu, Quanqing (2015). Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion. World Wide Web, 19 (6), 1-28. doi: 10.1007/s11280-015-0379-4

LCARS: A spatial item recommender system

Yin, Hongzhi, Cui, Bin, Sun, Yizhou, Hu, Zhiting and Chen, Ling (2014). LCARS: A spatial item recommender system. ACM Transactions on Information Systems, 32 (3) 11, 11-11. doi: 10.1145/2629461

Budgeted Embedding Table For Recommender Systems

Qu, Yunke, Chen, Tong, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2024). Budgeted Embedding Table For Recommender Systems. New York, NY, USA: ACM. doi: 10.1145/3616855.3635778

Defense Against Model Extraction Attacks on Recommender Systems

Zhang, Sixiao, Yin, Hongzhi, Chen, Hongxu and Long, Cheng (2024). Defense Against Model Extraction Attacks on Recommender Systems. New York, NY, USA: ACM. doi: 10.1145/3616855.3635751

Motif-based Prompt Learning for Universal Cross-domain Recommendation

Hao, Bowen, Yang, Chaoqun, Guo, Lei, Yu, Junliang and Yin, Hongzhi (2024). Motif-based Prompt Learning for Universal Cross-domain Recommendation. New York, NY, USA: ACM. doi: 10.1145/3616855.3635754

Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge

Qiu, Wei, Yin, He, Wu, Yuru, Zeng, Chujie, Chen, Chang, Dong, Yuqing and Liu, Yilu (2024). Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge. IEEE. doi: 10.1109/isgt59692.2024.10454172

Design of A Hybrid Compression Algorithm for High-fidelity Synchro-waveform Measurements

Qiu, Wei, Yin, He, Wu, Yuru, Dong, Yuqing, Zheng, Yao, Yao, Wenxuan and Liu, Yilu (2023). Design of A Hybrid Compression Algorithm for High-fidelity Synchro-waveform Measurements. IEEE. doi: 10.1109/etfg55873.2023.10407459

Model-free based Real-time Authentication Framework for Distributed Synchrophasors

Zheng, Yao, Qiu, Wei, Yao, Wenxuan, Li, Bing, Duan, Junfeng and Yin, He (2023). Model-free based Real-time Authentication Framework for Distributed Synchrophasors. IEEE. doi: 10.1109/etfg55873.2023.10407596

Learning Compact Compositional Embeddings via Regularized Pruning for Recommendation

Liang, Xurong, Chen, Tong, Nguyen, Quoc Viet Hung, Li, Jianxin and Yin, Hongzhi (2023). Learning Compact Compositional Embeddings via Regularized Pruning for Recommendation. 23rd IEEE International Conference on Data Mining (IEEE ICDM), Shanghai Peoples R China, Dec 01-04, 2023. LOS ALAMITOS: IEEE. doi: 10.1109/icdm58522.2023.00047

To predict or to reject: causal effect estimation with uncertainty on networked data

Wen, Hechuan, Chen, Tong, Chai, Li Kheng, Sadiq, Shazia, Zheng, Kai and Yin, Hongzhi (2023). To predict or to reject: causal effect estimation with uncertainty on networked data. 23rd IEEE International Conference on Data Mining (IEEE ICDM), Shanghai, China, 1 - 4 December 2023. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/icdm58522.2023.00184

Causality-guided graph learning for session-based recommendation

Yu, Dianer, Li, Qian, Yin, Hongzhi and Xu, Guandong (2023). Causality-guided graph learning for session-based recommendation. 32nd ACM International Conference on Information and Knowledge Management, Birmingham, United Kingdom, 21–25 October 2023. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3583780.3614803

Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph

Liu, Yi, Xuan, Hongrui, Li, Bohan, Wang, Meng, Chen, Tong and Yin, Hongzhi (2023). Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph. New York, NY, USA: ACM. doi: 10.1145/3583780.3615054

Semantic-aware node synthesis for imbalanced heterogeneous information networks

Gao, Xinyi, Zhang, Wentao, Chen, Tong, Yu, Junliang, Nguyen, Hung Quoc Viet and Yin, Hongzhi (2023). Semantic-aware node synthesis for imbalanced heterogeneous information networks. 32nd ACM International Conference on Information and Knowledge Management, Birmingham, United Kingdom, 21–25 October 2023. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3583780.3615055

Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation

Xia, Xin, Yu, Junliang, Xu, Guandong and Yin, Hongzhi (2023). Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation. New York, NY, USA: ACM. doi: 10.1145/3583780.3615088

Efficient bi-level optimization for recommendation denoising

Wang, Zongwei, Gao, Min, Li, Wentao, Yu, Junliang, Guo, Linxin and Yin, Hongzhi (2023). Efficient bi-level optimization for recommendation denoising. 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, CA, United States, 6-10 August 2023. New York, NY, United States: ACM. doi: 10.1145/3580305.3599324

Continuous input embedding size search for recommender systems

Qu, Yunke, Chen, Tong, Zhao, Xiangyu, Cui, Lizhen, Zheng, Kai and Yin, Hongzhi (2023). Continuous input embedding size search for recommender systems. The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, 23-27 July 2023. New York, NY, United States: ACM. doi: 10.1145/3539618.3591653

DREAM: adaptive reinforcement learning based on attention mechanism for temporal knowledge graph reasoning

Zheng, Shangfei, Yin, Hongzhi, Chen, Tong, Nguyen, Quoc Viet Hung, Chen, Wei and Zhao, Lei (2023). DREAM: adaptive reinforcement learning based on attention mechanism for temporal knowledge graph reasoning. 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, 23–27 July 2023. New York, NY, United States: ACM. doi: 10.1145/3539618.3591671

Manipulating federated recommender systems: poisoning with synthetic users and its countermeasures

Yuan, Wei, Nguyen, Quoc Viet Hung, He, Tieke, Chen, Liang and Yin, Hongzhi (2023). Manipulating federated recommender systems: poisoning with synthetic users and its countermeasures. 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, 23-27 July 2023. New York, NY, United States: ACM. doi: 10.1145/3539618.3591722

Model-agnostic decentralized collaborative learning for on-device POI recommendation

Long, Jing, Chen, Tong, Nguyen, Quoc Viet Hung, Xu, Guandong, Zheng, Kai and Yin, Hongzhi (2023). Model-agnostic decentralized collaborative learning for on-device POI recommendation. 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, 23-27 July 2023. New York, NY, United States: ACM. doi: 10.1145/3539618.3591733

KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment

Wang, Lingzhi, Chen, Tong, Yuan, Wei, Zeng, Xingshan, Wong, Kam-Fai and Yin, Hongzhi (2023). KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment. 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, 9 - 14 July 2023. Stroudsburg, PA United States: Association for Computational Linguistics.

Interaction-level membership inference attack against federated recommender systems

Yuan, Wei, Yang, Chaoqun, Nguyen, Quoc Viet Hung, Cui, Lizhen, He, Tieke and Yin, Hongzhi (2023). Interaction-level membership inference attack against federated recommender systems. The ACM Web Conference 2023, Austin, TX, United States, 30 April - 4 May 2023. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3543507.3583359

Semi-decentralized federated ego graph learning for recommendation

Qu, Liang, Tang, Ningzhi, Zheng, Ruiqi, Nguyen, Quoc Viet Hung, Huang, Zi, Shi, Yuhui and Yin, Hongzhi (2023). Semi-decentralized federated ego graph learning for recommendation. The ACM Web Conference 2023, Austin, TX, United States, 30 April - 4 May 2023. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3543507.3583337

Disconnected emerging knowledge graph oriented inductive link prediction

Zhang, Yufeng, Wang, Weiqing, Yin, Hongzhi, Zhao, Pengpeng, Chen, Wei and Zhao, Lei (2023). Disconnected emerging knowledge graph oriented inductive link prediction. 2023 IEEE 39th International Conference on Data Engineering (ICDE), Anaheim, CA, United States, 3-7 April 2023. Piscataway, NJ, United States: IEEE. doi: 10.1109/icde55515.2023.00036

Efficient and effective multi-modal queries through heterogeneous network embedding (extended abstract)

Tam Nguyen, Thanh, Thang Duong, Chi, Yin, Hongzhi, Weidlich, Matthias, Son Mai, Thai, Aberer, Karl and Viet Hung Nguyen, Quoc (2023). Efficient and effective multi-modal queries through heterogeneous network embedding (extended abstract). 39th International Conference on Data Engineering (ICDE), Anaheim, CA, United States, 3-7 April 2023. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/icde55515.2023.00322

MMKGR: Multi-hop multi-modal knowledge graph reasoning

Zheng, Shangfei, Wang, Weiqing, Qu, Jianfeng, Yin, Hongzhi, Chen, Wei and Zhao, Lei (2023). MMKGR: Multi-hop multi-modal knowledge graph reasoning. 2023 IEEE 39th International Conference on Data Engineering (ICDE), Anaheim, CA, United States, 3-7 April 2023. Piscataway, NJ, United States: IEEE. doi: 10.1109/icde55515.2023.00015

Federated unlearning for on-device recommendation

Yuan, Wei, Yin, Hongzhi, Wu, Fangzhao, Zhang, Shijie, He, Tieke and Wang, Hao (2023). Federated unlearning for on-device recommendation. Sixteenth ACM International Conference on Web Search and Data Mining, Singapore, Singapore, 27 February - 3 March 2023. New York, NY, United States: ACM. doi: 10.1145/3539597.3570463

Knowledge enhancement for contrastive multi-behavior recommendation

Xuan, Hongrui, Liu, Yi, Li, Bohan and Yin, Hongzhi (2023). Knowledge enhancement for contrastive multi-behavior recommendation. Sixteenth ACM International Conference on Web Search and Data Mining, Singapore, Singapore, 27 February - 3 March 2023. New York, NY, United States: ACM. doi: 10.1145/3539597.3570386

Learning to distill graph neural networks

Yang, Cheng, Guo, Yuxin, Xu, Yao, Shi, Chuan, Liu, Jiawei, Wang, Chunchen, Li, Xin, Guo, Ning and Yin, Hongzhi (2023). Learning to distill graph neural networks. Sixteenth ACM International Conference on Web Search and Data Mining, Singapore, Singapore, 27 February - 3 March 2023. New York, NY, United States: ACM. doi: 10.1145/3539597.3570480

Simplifying graph-based collaborative filtering for recommendation

He, Li, Wang, Xianzhi, Wang, Dingxian, Zou, Haoyuan, Yin, Hongzhi and Xu, Guandong (2023). Simplifying graph-based collaborative filtering for recommendation. Sixteenth ACM International Conference on Web Search and Data Mining, Singapore, Singapore, 27 February - 3 March 2023. New York, NY, United States: ACM. doi: 10.1145/3539597.3570451

Beyond double ascent via recurrent neural tangent kernel in sequential recommendation

Qiu, Ruihong, Huang, Zi and Yin, Hongzhi (2023). Beyond double ascent via recurrent neural tangent kernel in sequential recommendation. 22nd IEEE International Conference on Data Mining (ICDM), Orlando, FL USA, 28 November-1 December 2022. New York, NY USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/icdm54844.2022.00053

Imbalanced node classification beyond homophilic assumption

Liu, Jie, He, Mengting, Wang, Guangtao, Nguyen, Quoc Viet Hung, Shang, Xuequn and Yin, Hongzhi (2023). Imbalanced node classification beyond homophilic assumption. Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23), Macao, SAR, 19-25 August 2023. Palo Alto, CA, United States: AAAI Press. doi: 10.24963/ijcai.2023/848

Locality aware temporal FMs for crime prediction

Mansha, Sameen, Rehman, Abdur, Abdullah, Shaaf, Kamiran, Faisal and Yin, Hongzhi (2022). Locality aware temporal FMs for crime prediction. 31st ACM International Conference on Information and Knowledge Management, Atlanta, GA, United States, 17-21 October 2022. New York, NY, United States: ACM. doi: 10.1145/3511808.3557657

CIRCLE: continual repair across programming languages

Yuan, Wei, Zhang, Quanjun, He, Tieke, Fang, Chunrong, Hung, Nguyen Quoc Viet, Hao, Xiaodong and Yin, Hongzhi (2022). CIRCLE: continual repair across programming languages. ISSTA '22: 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, Online, 18 - 22 July 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3533767.3534219

Are Graph Augmentations Necessary? : Simple Graph Contrastive Learning for Recommendation

Yu, Junliang, Yin, Hongzhi, Xia, Xin, Chen, Tong, Cui, Lizhen and Nguyen, Quoc Viet Hung (2022). Are Graph Augmentations Necessary? : Simple Graph Contrastive Learning for Recommendation. SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11 - 15 July 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3477495.3531937

On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation

Xia, Xin, Yin, Hongzhi, Yu, Junliang, Wang, Qinyong, Xu, Guandong and Nguyen, Quoc Viet Hung (2022). On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation. SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11 - 15 July 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3477495.3531775

Single-shot Embedding Dimension Search in Recommender System

Qu, Liang, Ye, Yonghong, Tang, Ningzhi, Zhang, Lixin, Shi, Yuhui and Yin, Hongzhi (2022). Single-shot Embedding Dimension Search in Recommender System. SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11 - 15 July 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3477495.3532060

Thinking inside The Box : Learning Hypercube Representations for Group Recommendation

Chen, Tong, Yin, Hongzhi, Long, Jing, Nguyen, Quoc Viet Hung, Wang, Yang and Wang, Meng (2022). Thinking inside The Box : Learning Hypercube Representations for Group Recommendation. SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11 - 15 July 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3477495.3532066

Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations

Zhang, Yan, Li, Changyu, Tsang, Ivor W., Xu, Hui, Duan, Lixin, Yin, Hongzhi, Li, Wen and Shao, Jie (2022). Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations. 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia, 9-12 May 2022. Piscataway, NJ United States: IEEE. doi: 10.1109/icde53745.2022.00265

Network Alignment with Holistic Embeddings (Extended Abstract)

Huynh, Thanh Trung, Duong, Thang Chi, Nguyen, Thanh Tam, Tong, Van Vinh, Sattar, Abdul, Yin, Hongzhi and Nguyen, Quoc Viet Hung (2022). Network Alignment with Holistic Embeddings (Extended Abstract). 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia, 9-12 May 2022. Piscataway, NJ United States: IEEE. doi: 10.1109/icde53745.2022.00131

Uniting heterogeneity, inductiveness, and efficiency for graph representation learning (Extended Abstract)

Chen, Tong, Yin, Hongzhi, Ren, Jie, Huang, Zi, Zhang, Xiangliang and Wang, Hao (2022). Uniting heterogeneity, inductiveness, and efficiency for graph representation learning (Extended Abstract). 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia, 9-12 May 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/icde53745.2022.00143

exRumourLens: Auditable Rumour Detection with Multi-View Explanations

Phan, Thanh Cong, Nguyen, Thanh Tam, Weidlich, Matthias, Yin, Hongzhi, Jo, Jun and Nguyen, Quoc Viet Hung (2022). exRumourLens: Auditable Rumour Detection with Multi-View Explanations. 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia, 9-12 May 2022. Piscataway, NJ United States: IEEE. doi: 10.1109/icde53745.2022.00291

Accepted Tutorials at The Web Conference 2022

Tommasini, Riccardo, Roy, Senjuti Basu, Wang, Xuan, Wang, Hongwei, Ji, Heng, Han, Jiawei, Nakov, Preslav, Da San Martino, Giovanni, Alam, Firoj, Schedl, Markus, Lex, Elisabeth, Bharadwaj, Akash, Cormode, Graham, Dojchinovski, Milan, Forberg, Jan, Frey, Johannes, Bonte, Pieter, Balduini, Marco, Belcao, Matteo, Della Valle, Emanuele, Yu, Junliang, Yin, Hongzhi, Chen, Tong, Liu, Haochen, Wang, Yiqi, Fan, Wenqi, Liu, Xiaorui, Dacon, Jamell, Lye, Lingjuan ... He, Xiangnan (2022). Accepted Tutorials at The Web Conference 2022. The Web Conference 2022, Lyon, France, 25 – 29 April 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3487553.3547182

ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs

Wang, Yanling, Zhang, Jing, Li, Haoyang, Dong, Yuxiao, Yin, Hongzhi, Li, Cuiping and Chen, Hong (2022). ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs. WWW '22: The ACM Web Conference 2022, Lyon, France, 25 - 29 April 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3485447.3512207

Unified question generation with continual lifelong learning

Yuan, Wei, Yin, Hongzhi, He, Tieke, Chen, Tong, Wang, Qiufeng and Cui, Lizhen (2022). Unified question generation with continual lifelong learning. WWW 2022 - ACM Web Conference 2022, Virtual Event, Lyon, France, 25-29 April 2022. New York, United States: Association for Computing Machinery. doi: 10.1145/3485447.3511930

Contrastive learning for representation degeneration problem in sequential recommendation

Qiu, Ruihong, Huang, Zi, Yin, Hongzhi and Wang, Zijian (2022). Contrastive learning for representation degeneration problem in sequential recommendation. WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, Virtual, AZ, United States, 21 - 25 February 2022. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3488560.3498433

PipA!ack: poisoning federated recommender systems for manipulating item promotion

Zhang, Shijie, Yin, Hongzhi, Chen, Tong, Huang, Zi, Nguyen, Quoc Viet Hung and Cui, Lizhen (2022). PipA!ack: poisoning federated recommender systems for manipulating item promotion. WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, Virtual, AZ, United States, 21 - 25 February 2022. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3488560.3498386

Hyperbolic personalized tag recommendation

Zhao, Weibin, Zhang, Aoran, Shang, Lin, Yu, Yonghong, Zhang, Li, Wang, Can, Chen, Jiajun and Yin, Hongzhi (2022). Hyperbolic personalized tag recommendation. 27th International Conference on Database Systems for Advanced Applications (DASFAA-2022), Virtual , 11-14 April 2022. Heidelberg, Germany: Springer. doi: 10.1007/978-3-031-00126-0_14

Switchable online knowledge distillation

Qian, Biao, Wang, Yang, Yin, Hongzhi, Hong, Richang and Wang, Meng (2022). Switchable online knowledge distillation. 17th European Conference on Computer Vision, ECCV 2022, Tel Aviv, Israel, October 23-27, 2022. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-20083-0_27

A knowledge-aware recommender with attention-enhanced dynamic convolutional network

Liu, Yi, Li, Bohan, Zang, Yalei, Li, Aoran and Yin, Hongzhi (2021). A knowledge-aware recommender with attention-enhanced dynamic convolutional network. CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual, 1-5 November 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3459637.3482406

Double-scale self-supervised hypergraph learning for group recommendation

Zhang, Junwei, Gao, Min, Yu, Junliang, Guo, Lei, Li, Jundong and Yin, Hongzhi (2021). Double-scale self-supervised hypergraph learning for group recommendation. CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual, 1-5 November 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3459637.3482426

GDFM: Gene Vectors Embodied Deep Attentional Factorization Machines for Interaction prediction

Mansha, Sameen, Khalid, Tayyab, Kamiran, Faisal, Hussain, Masroor, Hussain, Syed Fawad and Yin, Hongzhi (2021). GDFM: Gene Vectors Embodied Deep Attentional Factorization Machines for Interaction prediction. CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual, 1-5 November 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3459637.3482110

International Workshop on Privacy, Security and Trust in Computational Intelligence (PSTCI2021)

Zhang, Xuyun, Puthal, Deepak Kumar, Yang, Chi, Choo, Kim-Kwang Raymond, Yin, Hongzhi and Liu, Guanfeng (2021). International Workshop on Privacy, Security and Trust in Computational Intelligence (PSTCI2021). CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual, 1-5 November 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3459637.3482043

Lightweight self-attentive sequential recommendation

Li, Yang, Chen, Tong, Zhang, Peng-Fei and Yin, Hongzhi (2021). Lightweight self-attentive sequential recommendation. CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual, 1-5 November 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3459637.3482448

Self-supervised graph co-training for session-based recommendation

Cui, Lizhen, Shao, Yingxia, Yu, Junliang, Yin, Hongzhi and Xia, Xin (2021). Self-supervised graph co-training for session-based recommendation. CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual, 1-5 November 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3459637.3482388

CausalRec: causal inference for visual debiasing in visually-aware recommendation

Qiu, Ruihong, Wang, Sen, Chen, Zhi, Yin, Hongzhi and Huang, Zi (2021). CausalRec: causal inference for visual debiasing in visually-aware recommendation. MM '21: ACM Multimedia Conference, Virtual, 20-24 October 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3474085.3475266

Joint-teaching: learning to refine knowledge for resource-constrained unsupervised cross-modal retrieval

Zhang, Peng-Fei, Duan, Jiasheng, Huang, Zi and Yin, Hongzhi (2021). Joint-teaching: learning to refine knowledge for resource-constrained unsupervised cross-modal retrieval. MM '21: ACM Multimedia Conference, Virtual, 20-24 October 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3474085.3475286

ImGAGN: imbalanced network embedding via generative adversarial graph networks

Qu, Liang, Zhu, Huaisheng, Zheng, Ruiqi, Shi, Yuhui and Yin, Hongzhi (2021). ImGAGN: imbalanced network embedding via generative adversarial graph networks. 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Virtual (Singapore), 14-18 August 2021. New York, NY, United States: ACM. doi: 10.1145/3447548.3467334

Learning elastic embeddings for customizing on-device recommenders

Chen, Tong, Yin, Hongzhi, Zheng, Yujia, Huang, Zi, Wang, Yang and Wang, Meng (2021). Learning elastic embeddings for customizing on-device recommenders. 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Virtual (Singapore), 14-18 August 2021. New York, NY, United States: ACM. doi: 10.1145/3447548.3467220

Socially-aware self-supervised tri-training for recommendation

Yu, Junliang, Yin, Hongzhi, Gao, Min, Xia, Xin, Zhang, Xiangliang and Viet Hung, Nguyen Quoc (2021). Socially-aware self-supervised tri-training for recommendation. 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Virtual (Singapore), 14-18 August 2021. New York, NY, United States: ACM. doi: 10.1145/3447548.3467340

Heterogeneous hypergraph embedding for graph classification

Sun, Xiangguo, Yin, Hongzhi, Liu, Bo, Chen, Hongxu, Cao, Jiuxin, Shao, Yingxia and Viet Hung, Nguyen Quoc (2021). Heterogeneous hypergraph embedding for graph classification. WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Virtual Event, 8-12 March 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3437963.3441835

Pre-training graph neural networks for cold-start users and items representation

Hao, Bowen, Zhang, Jing, Yin, Hongzhi, Li, Cuiping and Chen, Hong (2021). Pre-training graph neural networks for cold-start users and items representation. WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Virtual Event, 8-12 March 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3437963.3441738

Temporal meta-path guided explainable recommendation

Chen, Hongxu, Li, Yicong, Sun, Xiangguo, Xu, Guandong and Yin, Hongzhi (2021). Temporal meta-path guided explainable recommendation. WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Virtual Event Israel, 8 - 12 March 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3437963.3441762

DA-GCN: a domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation

Guo, Lei, Tang, Li, Chen, Tong, Zhu, Lei, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2021). DA-GCN: a domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. International Joint Conference on Artificial Intelligence, Montreal, Canada, 19-27 August 2021. San Francisco, CA, United States: International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2021/342

Discovering collaborative signals for next POI recommendation with iterative Seq2Graph augmentation

Li, Yang, Chen, Tong, Luo, Yadan, Yin, Hongzhi and Huang, Zi (2021). Discovering collaborative signals for next POI recommendation with iterative Seq2Graph augmentation. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal, QC Canada, 19 - 27 August 2021. Palo Alto, CA United States: A A A I Press. doi: 10.24963/ijcai.2021/206

Where are we in embedding spaces?

Zhang, Sixiao, Chen, Hongxu, Ming, Xiao, Cui, Lizhen, Yin, Hongzhi and Xu, Guandong (2021). Where are we in embedding spaces?. KDD '21: 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore, Singapore, 14 - 18 August 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3447548.3467421

Decoupling representation learning and classification for GNN-based anomaly detection

Wang, Yanling, Zhang, Jing, Guo, Shasha, Yin, Hongzhi, Li, Cuiping and Chen, Hong (2021). Decoupling representation learning and classification for GNN-based anomaly detection. International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual, 11-15 July 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3404835.3462944

Enhancing domain-level and user-level adaptivity in diversified recommendation

Liang, Yile, Qian, Tieyun, Li, Qing and Yin, Hongzhi (2021). Enhancing domain-level and user-level adaptivity in diversified recommendation. SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, 11-15 July 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3404835.3462957

Learning to ask appropriate questions in conversational recommendation

Ren, Xuhui, Yin, Hongzhi, Chen, Tong, Wang, Hao, Huang, Zi and Zheng, Kai (2021). Learning to ask appropriate questions in conversational recommendation. SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, 11-15 July 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3404835.3462839

Privacy protection in deep multi-modal retrieval

Zhang, Peng-Fei, Li, Yang, Huang, Zi and Yin, Hongzhi (2021). Privacy protection in deep multi-modal retrieval. 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual, 11-15 July 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3404835.3462837

Self-supervised hypergraph convolutional networks for session-based recommendation

Xia, Xin, Yin, Hongzhi, Yu, Junliang, Wang, Qinyong, Cui, Lizhen and Zhang, Xiangliang (2021). Self-supervised hypergraph convolutional networks for session-based recommendation. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Virtual, 2-9 February 2021. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence.

Adapting to context-aware knowledge in natural conversation for multi-turn response selection

Zhang, Chen, Wang, Hao, Jiang, Feijun and Yin, Hongzhi (2021). Adapting to context-aware knowledge in natural conversation for multi-turn response selection. WWW '21: Proceedings of the Web Conference 2021, Ljubljana, Slovenia, 19-23 April 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3442381.3449902

Graph embedding for recommendation against attribute inference attacks

Zhang, Shijie, Yin, Hongzhi, Chen, Tong, Huang, Zi, Cui, Lizhen and Zhang, Xiangliang (2021). Graph embedding for recommendation against attribute inference attacks. WWW '21: Proceedings of the Web Conference 2021, Ljubljana, Slovenia, 19-22 April 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3442381.3449813

Multi-level hyperedge distillation for social linking prediction on sparsely observed networks

Sun, Xiangguo, Yin, Hongzhi, Liu, Bo, Chen, Hongxu, Meng, Qing, Han, Wang and Cao, Jiuxin (2021). Multi-level hyperedge distillation for social linking prediction on sparsely observed networks. WWW '21: Proceedings of the Web Conference 2021, Ljubljana, Slovenia, 19-23 April 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3442381.3449912

Self-supervised multi-channel hypergraph convolutional network for social recommendation

Yu, Junliang, Yin, Hongzhi, Li, Jundong, Wang, Qinyong, Hung, Nguyen Quoc Viet and Zhang, Xiangliang (2021). Self-supervised multi-channel hypergraph convolutional network for social recommendation. WWW '21: Proceedings of the Web Conference 2021, Ljubljana, Slovenia, 19-23 April 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3442381.3449844

DDHH: A decentralized deep learning framework for large-scale heterogeneous networks

Imran, Mubashir, Yin, Hongzhi, Chen, Tong, Huang, Zi, Zhang, Xiangliang and Zheng, Kai (2021). DDHH: A decentralized deep learning framework for large-scale heterogeneous networks. 2021 IEEE 37th International Conference on Data Engineering (ICDE), Chania, Greece, 19-22 April 2021. Washington, DC USA: IEEE Computer Society. doi: 10.1109/ICDE51399.2021.00196

Entity alignment for knowledge graphs with multi-order convolutional networks (extended abstract)

Tam, Nguyen Thanh, Trung, Huynh Thanh, Yin, Hongzhi, Van Vinh, Tong, Sakong, Darnbi, Zheng, Bolong and Hung, Nguyen Quoc Viet (2021). Entity alignment for knowledge graphs with multi-order convolutional networks (extended abstract). 2021 IEEE 37th International Conference on Data Engineering (ICDE), Chania, Greece, 19-22 April 2021. Washington, DC USA: IEEE Computer Society. doi: 10.1109/ICDE51399.2021.00247

Gallat: A spatiotemporal graph attention network for passenger demand prediction

Wang, Yuandong, Yin, Hongzhi, Chen, Tong, Liu, Chunyang, Wang, Ben, Wo, Tianyu and Xu, Jie (2021). Gallat: A spatiotemporal graph attention network for passenger demand prediction. 2021 IEEE 37th International Conference on Data Engineering (ICDE), Chania, Greece, 19-22 April 2021. Washington, DC USA: IEEE Computer Society. doi: 10.1109/ICDE51399.2021.00212

Reliable recommendation with review-level explanations

Lyu, Yanzhang, Yin, Hongzhi, Liu, Jun, Liu, Mengyue, Liu, Huan and Deng, Shizhuo (2021). Reliable recommendation with review-level explanations. 2021 IEEE 37th International Conference on Data Engineering (ICDE), Chania, Greece, 19-22 April 2021. Washington, DC USA: IEEE Computer Society. doi: 10.1109/ICDE51399.2021.00137

Memory augmented multi-instance contrastive predictive coding for sequential recommendation

Qiu, Ruihong, Huang, Zi and Yin, Hongzhi (2021). Memory augmented multi-instance contrastive predictive coding for sequential recommendation. IEEE International Conference on Data Mining, Auckland, New Zealand, 7-10 December 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDM51629.2021.00063

Recommending courses in MOOCs for jobs: an auto weak supervision approach

Hao, Bowen, Zhang, Jing, Li, Cuiping, Chen, Hong and Yin, Hongzhi (2021). Recommending courses in MOOCs for jobs: an auto weak supervision approach. European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, Virtual, 14-18 September 2021. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-030-67667-4_3

Self-supervised hypergraph convolutional networks for session-based recommendation

Xia, Xin, Yin, Hongzhi, Yu, Junliang, Wang, Qinyong, Cui, Lizhen and Zhang, Xiangliang (2021). Self-supervised hypergraph convolutional networks for session-based recommendation. 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence, Virtual, 2-9 February 2021. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence Press.

Subgraph convolutional network for recommendation

Zhao, Yan, Zhou, Lianming, Deng, Liwei, Zheng, Vincent W., Yin, Hongzhi and Zheng, Kai (2021). Subgraph convolutional network for recommendation. 2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS), Xi'an, China, 7-8 November 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/CCIS53392.2021.9754683

Multi-level graph convolutional networks for cross-platform Anchor Link Prediction

Chen, Hongxu, Yin, Hongzhi, Sun, Xiangguo, Chen, Tong, Gabrys, Bogdan and Musial, Katarzyna (2020). Multi-level graph convolutional networks for cross-platform Anchor Link Prediction. ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, CA, United States, 23-27 August 2020. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3394486.3403201

FactCatch: incremental pay-as-you-go fact checking with minimal user effort

Nguyen, Thanh Tam, Weidlich, Matthias, Yin, Hongzhi, Zheng, Bolong, Nguyen, Quang Huy and Nguyen, Quoc Viet Hung (2020). FactCatch: incremental pay-as-you-go fact checking with minimal user effort. International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event China, 25-30 July 2020. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3397271.3401408

GAG: global attributed graph neural network for streaming session-based recommendation

Qiu, Ruihong, Yin, Hongzhi, Huang, Zi and Chen, Tong (2020). GAG: global attributed graph neural network for streaming session-based recommendation. International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event China , 25-30 July 2020. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3397271.3401109

Try this instead: personalized and interpretable substitute recommendation

Chen, Tong, Yin, Hongzhi, Ye, Guanhua, Huang, Zi, Wang, Yang and Wang, Meng (2020). Try this instead: personalized and interpretable substitute recommendation. International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event China, 25-30 July 2020. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3397271.3401042

Discovering subsequence patterns for next POI recommendation

Zhao, Kangzhi, Zhang, Yong, Yin, Hongzhi, Wang, Jin, Zheng, Kai, Zhou, Xiaofang and Xing, Chunxiao (2020). Discovering subsequence patterns for next POI recommendation. Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), Yokohama, Japan, 11-17 July, 2020. California, United States: International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2020/445

Adaptive network alignment with unsupervised and multi-order convolutional networks

Trung, Huynh Thanh, Van Vinh, Tong, Tam, Nguyen Thanh, Yin, Hongzhi, Weidlich, Matthias and Viet Hung, Nguyen Quoc (2020). Adaptive network alignment with unsupervised and multi-order convolutional networks. 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, United States, 20-24 April 2020. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/ICDE48307.2020.00015

Decentralized embedding framework for large-scale networks

Imran, Mubashir, Yin, Hongzhi, Chen, Tong, Shao, Yingxia, Zhang, Xiangliang and Zhou, Xiaofang (2020). Decentralized embedding framework for large-scale networks. International Conference on Database Systems for Advanced Applications, Jeju, South Korea, 24-27 September 2020. Heidelberg, Germany: Springer . doi: 10.1007/978-3-030-59419-0_26

EPARS: Early prediction of at-risk students with online and offline learning behaviors

Yang, Yu, Wen, Zhiyuan, Cao, Jiannong, Shen, Jiaxing, Yin, Hongzhi and Zhou, Xiaofang (2020). EPARS: Early prediction of at-risk students with online and offline learning behaviors. International Conference on Database Systems for Advanced Applications, Jeju, South Korea, 24-27 September 2020. Heidelberg, Germany: Springer . doi: 10.1007/978-3-030-59416-9_1

GCN-based user representation learning for unifying robust recommendation and fraudster detection

Zhang, Shijie, Yin, Hongzhi, Chen, Tong, Hung, Quoc Viet Nguyen, Huang, Zi and Cui, Lizhen (2020). GCN-based user representation learning for unifying robust recommendation and fraudster detection. SIGIR '20: 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Online, July 2020. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3397271.3401165

Graph embeddings for one-pass processing of heterogeneous queries

Duong, Chi Thang, Yin, Hongzhi, Hoang, Dung, Nguyen, Minn Hung, Weidlich, Matthias, Hung Nguyen, Quoc Viet and Aberer, Karl (2020). Graph embeddings for one-pass processing of heterogeneous queries. 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, United States, 20-24 April 2020. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/ICDE48307.2020.00222

Group recommendation with latent voting mechanism

Guo, Lei, Yin, Hongzhi, Wang, Qinyong, Cui, Bin, Huang, Zi and Cui, Lizhen (2020). Group recommendation with latent voting mechanism. 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, United States, 20-24 April 2020. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/ICDE48307.2020.00018

Neural pairwise ranking factorization machine for item recommendation

Jiao, Lihong, Yu, Yonghong, Zhou, Ningning, Zhang, Li and Yin, Hongzhi (2020). Neural pairwise ranking factorization machine for item recommendation. International Conference on Database Systems for Advanced Applications, Jeju, South Korea, 24-27 September 2020. Heidelberg, Germany: Springer . doi: 10.1007/978-3-030-59410-7_46

Next point-of-interest recommendation on resource-constrained mobile devices

Wang, Qinyong, Yin, Hongzhi, Chen, Tong, Huang, Zi, Wang, Hao, Zhao, Yanchang and Viet Hung, Nguyen Quoc (2020). Next point-of-interest recommendation on resource-constrained mobile devices. WWW '20: The Web Conference 2020, Taipei, Taiwan, April 2020. New York, United States: Association for Computing Machinery. doi: 10.1145/3366423.3380170

Sequence-aware factorization machines for temporal predictive analytics

Chen, Tong, Yin, Hongzhi, Nguyen, Quoc Viet Hung, Peng, Wen-Chih, Li, Xue and Zhou, Xiaofang (2020). Sequence-aware factorization machines for temporal predictive analytics. 2020 IEEE 36th International Conference on Data Engineering, Dallas, Texas, United States, 20-24 April 2020. LOS ALAMITOS: IEEE Computer Society. doi: 10.1109/ICDE48307.2020.00125

Where to go next: modeling long- and short-term user preferences for point-of-interest recommendation

Sun, Ke, Qian, Tieyun, Chen, Tong, Liang, Yile, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2020). Where to go next: modeling long- and short-term user preferences for point-of-interest recommendation. AAAI Conference on Artificial Intelligence, New York, NY, United States, 7-12 February 2020. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v34i01.5353

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

Shang, Mingyue, Fu, Zhenxin, Yin, Hongzhi, Tang, Bo, Zhao, Dongyan and Yan, Rui (2019). Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?. The Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, HI United States, 27 January – 1 February 2019. Menlo Park, CA United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v33i01.330110031

AIR: Attentional intention-aware recommender systems

Chen, Tong, Yin, Hongzhi, Chen, Hongxu, Yan, Rui, Nguyen, Quoc Viet Hung and Li, Xue (2019). AIR: Attentional intention-aware recommender systems. IEEE 35th International Conference on Data Engineering (ICDE), Macau, China, 8-11 April 2019. Piscataway, NJ United States: IEEE Computer Society. doi: 10.1109/ICDE.2019.00035

BLOMA: explain collaborative filtering via Boosted Local rank-One Matrix Approximation

Gao, Chongming, Yuan, Shuai, Zhang, Zhong, Yin, Hongzhi and Shao, Junming (2019). BLOMA: explain collaborative filtering via Boosted Local rank-One Matrix Approximation. 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22-25 April 2019. Philadelphia, PA, United States: Elsevier. doi: 10.1007/978-3-030-18590-9_72

Enhancing collaborative filtering with generative augmentation

Wang, Qinyong, Nguyen, Quoc Viet Hung, Yin, Hongzhi, Huang, Zi, Wang, Hao and Cui, Lizhen (2019). Enhancing collaborative filtering with generative augmentation. 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), Anchorage, AK United States, 4-8 August 2019. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3292500.3330873

Exploiting centrality information with graph convolutions for network representation learning

Chen, Hongxu, Yin, Hongzhi, Chen, Tong, Nguyen, Quoc Viet Hung, Peng, Wen-Chih and Li, Xue (2019). Exploiting centrality information with graph convolutions for network representation learning. IEEE 35th International Conference on Data Engineering (ICDE), Macau, China, 8-11 April 2019. Piscataway, NJ United States: IEEE Computer Society. doi: 10.1109/ICDE.2019.00059

Find a reasonable ending for stories: Does logic relation help the story cloze test?

Shang, Mingyue, Fu, Zhenxin, Yin, Hongzhi, Tang, Bo, Zhao, Dongyan and Yan, Rui (2019). Find a reasonable ending for stories: Does logic relation help the story cloze test?. 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, HI, United States, 27 January - 1 February, 2019. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence.

Generating reliable friends via adversarial training to improve social recommendation

Yu, Junliang, Gao, Min, Yin, Hongzhi, Li, Jundong, Gao, Chongming and Wang, Qinyong (2019). Generating reliable friends via adversarial training to improve social recommendation. IEEE International Conference on Data Mining , Beijing, China, 8-11 November 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDM.2019.00087

IWSC 2017 chairs' welcome

Zhang, Rui, Li, Jianxin, Yin, Hongzhi, Reynolds, Mark, Cheema, Muhammad Aamir and Chen, Ling (2019). IWSC 2017 chairs' welcome. International Conference on World Wide Web , Perth, WA, Australia, 3-7 April 2017. Geneva, Switzerland: International World Wide Web Conferences Steering Committee.

Inferring substitutable products with deep network embedding

Zhang, Shijie, Yin, Hongzhi, Wang, Qinyong, Chen, Tong, Chen, Hongxu and Nguyen, Quoc Viet Hung (2019). Inferring substitutable products with deep network embedding. International Joint Conference on Artificial Intelligence, Macao, China, 10-16 August 2019. California: International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2019/598

Multi-hop path queries over knowledge graphs with neural memory networks

Wang, Qinyong, Yin, Hongzhi, Wang, Weiqing, Huang, Zi, Guo, Guibing and Nguyen, Quoc Viet Hung (2019). Multi-hop path queries over knowledge graphs with neural memory networks. 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22 - 25 April 2019. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-18576-3_46

Online user representation learning across heterogeneous social networks

Wang, Weiqing, Yin, Hongzhi, Du, Xingzhong, Hua, Wen, Li, Yongjun and Nguyen, Quoc Viet Hung (2019). Online user representation learning across heterogeneous social networks. 42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), Paris, France, 21-25 July 2019. New York, NY, United States: Association for Computing Machinery (ACM). doi: 10.1145/3331184.3331258

Origin-destination matrix prediction via graph convolution: A new perspective of passenger demand modeling

Wang, Yuandong, Wo, Tianyu, Yin, Hongzhi, Xu, Jie, Chen, Hongxu and Zheng, Kai (2019). Origin-destination matrix prediction via graph convolution: A new perspective of passenger demand modeling. 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), Anchorage, AK United States, 4-8 August 2019. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3292500.3330877

Rethinking the item order in session-based recommendation with graph neural networks

Qiu, Ruihong, Li, Jingjing, Huang, Zi and Yin, Hongzhi (2019). Rethinking the item order in session-based recommendation with graph neural networks. CIKM '19 28th ACM International Conference on Information and Knowledge Management, Beijing, China, 3 - 7 November, 2019. New York, New York, USA: ACM Press. doi: 10.1145/3357384.3358010

Semi-supervised Clustering with Deep Metric Learning

Li, Xiaocui, Yin, Hongzhi, Zhou, Ke, Chen, Hongxu, Sadiq, Shazia and Zhou, Xiaofang (2019). Semi-supervised Clustering with Deep Metric Learning. 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22-15 April 2019. Heidelberg, Germany: Springer . doi: 10.1007/978-3-030-18590-9_50

Social influence-based group representation learning for group recommendation

Yin, Hongzhi, Wang, Qinyong, Zheng, Kai, Li, Zhixu, Yang, Jiali and Zhou, Xiaofang (2019). Social influence-based group representation learning for group recommendation. 35th International Conference on Data Engineering (ICDE 2019), Macao, Macao, 8-11 April 2019. New York, NY, United States: IEEE Computer Society. doi: 10.1109/ICDE.2019.00057

Streaming Session-based Recommendation

Guo, Lei, Chen, Tong, Yin, Hongzhi, Zhou, Alexander, Wang, Qinyong and Hung, Nguyen Quoc Viet (2019). Streaming Session-based Recommendation. 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), Anchorage, AK United States, 4-8 August 2019. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3292500.3330839

What can history tell us? Identifying relevant sessions for next-item recommendation

Sun, Ke, Qian, Tieyun, Yin, Hongzhi, Chen, Tong, Chen, Yiqi and Chen, Ling (2019). What can history tell us? Identifying relevant sessions for next-item recommendation. 28th ACM International Conference on Information and Knowledge Management, Beijing, China, 3-7 November 2019. New York, United States: Association for Computing Machinery. doi: 10.1145/3357384.3358050

A privacy-preserving framework for subgraph pattern matching in cloud

Gao, Jiuru, Xu, Jiajie, Liu, Guanfeng, Chen, Wei, Yin, Hongzhi and Zhao, Lei (2018). A privacy-preserving framework for subgraph pattern matching in cloud. 23rd International Conference on Database Systems for Advanced Applications DASFAA 2018, Gold Coast, QLD Australia, 21 - 24 May 2018. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-91452-7_20

Adaptive implicit friends identification over heterogeneous network for social recommendation

Yu, Junliang, Gao, Min, Li, Jundong, Yin, Hongzhi and Liu, Huan (2018). Adaptive implicit friends identification over heterogeneous network for social recommendation. 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, 22-26 October 2018. New York, NY, United States: Association for Computing Machinery (ACM). doi: 10.1145/3269206.3271725

Call attention to rumors: deep attention based recurrent neural networks for early rumor detection

Chen, Tong, Li, Xue, Yin, Hongzhi and Zhang, Jun (2018). Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018, Melbourne, VIC, Australia, 3 June 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-04503-6_4

Computing crowd consensus with partial agreement

Nguyen, Quoc Viet Hung, Huynh, Huu Viet, Nguyen, Thanh Tam, Weidlich, Matthias, Yin, Hongzhi and Zhou, Xiaofang (2018). Computing crowd consensus with partial agreement. 34th IEEE International Conference on Data Engineering (ICDE 2018), Paris, France, 16-19 April 2018. NEW YORK: IEEE. doi: 10.1109/ICDE.2018.00232

Discrete deep learning for fast content-aware recommendation

Zhang, Yan, Yin, Hongzhi, Huang, Zi, Du, Xingzhong, Yang, Guowu and Lian, Defu (2018). Discrete deep learning for fast content-aware recommendation. 11th ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, CA, United States, 5-9 February 2018. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3159652.3159688

Discrete ranking-based matrix factorization with self-paced learning

Zhang, Yan, Wang, Haoyu, Lian, Defu, Tsang, Ivor W., Yin, Hongzhi and Yang, Guowu (2018). Discrete ranking-based matrix factorization with self-paced learning. 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), London, United Kingdom, 19 - 23 August 2018. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3219819.3220116

Effective and efficient user account linkage across location based social networks

Chen, Wei, Yin, Hongzhi, Wang, Weiqing, Zhao, Lei and Zhou, Xiaofang (2018). Effective and efficient user account linkage across location based social networks. 34th IEEE International Conference on Data Engineering (ICDE 2018), Paris, France, 16-19 April 2018. NEW YORK: IEEE. doi: 10.1109/ICDE.2018.00101

Eliminating temporal conflicts in uncertain temporal knowledge graphs

Lu, Lingjiao, Fang, Junhua, Zhao, Pengpeng, Xu, Jiajie, Yin, Hongzhi and Zhao, Lei (2018). Eliminating temporal conflicts in uncertain temporal knowledge graphs. 19th International Conference on Web Information Systems Engineering, WISE 2018, Dubai, United Arab Emirates, November 12 - 15, 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-02922-7_23

Exploiting reshaping subgraphs from bilateral propagation graphs

Hosseini, Saeid, Yin, Hongzhi, Cheung, Ngai-Man, Leng, Kan Pak, Elovici, Yuval and Zhou, Xiaofang (2018). Exploiting reshaping subgraphs from bilateral propagation graphs. 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, Gold Coast, QLD, Australia, 21-24 May 2018. Cham, Switzerland: Springer Verlag. doi: 10.1007/978-3-319-91452-7_23

Extracting representative user subset of social networks towards user characteristics and topological features

Zhou, Yiming, Han, Yuehui, Liu, An, Li, Zhixu, Yin, Hongzhi and Zhao, Lei (2018). Extracting representative user subset of social networks towards user characteristics and topological features. 19th International Conference on Web Information Systems Engineering, WISE 2018, Dubai, United Arab Emirates, November 12 - 15, 2018. Cham, Switzerland: Springer . doi: 10.1007/978-3-030-02922-7_15

From anomaly detection to rumour detection using data streams of social platforms

Tam, Nguyen Thanh, Weidlich, Matthias, Zheng, Bolong, Yin, Hongzhi, Hung, Nguyen Quoc Viet and Stantic, Bela (2018). From anomaly detection to rumour detection using data streams of social platforms. 45th International Conference on Very Large Data Bases (VLDB 2019), Los Angeles, CA, United States, 26-30 August 2017. New York, NY, United States: Association for Computing Machinery (ACM). doi: 10.14778/3329772.3329778

Joint event-partner recommendation in event-based social networks

Yin, Hongzhi, Zou, Lei, Nguyen, Quoc Viet Hung, Huang, Zi and Zhou, Xiaofang (2018). Joint event-partner recommendation in event-based social networks. 34th IEEE International Conference on Data Engineering (ICDE 2018), Paris, France, 16-19 April 2018. NEW YORK: IEEE. doi: 10.1109/ICDE.2018.00088

LC-RNN: A deep learning model for traffic speed prediction

Lv, Zhongjian, Xu, Jiajie, Zheng, Kai, Yin, Hongzhi, Zhao, Pengpeng and Zhou, Xiaofang (2018). LC-RNN: A deep learning model for traffic speed prediction. 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm, Sweden, 13-19 July 2018. International Joint Conferences on Artificial Intelligence. doi: 10.24963/ijcai.2018/482

Look deeper see richer: Depth-aware image paragraph captioning

Wang, Ziwei, Luo, Yadan, Li, Yang, Huang, Zi and Yin, Hongzhi (2018). Look deeper see richer: Depth-aware image paragraph captioning. 26th ACM Multimedia conference, MM 2018, Seoul, South Korea, October 22 - 26, 2018. New York, NY, Untied States: Association for Computing Machinery, Inc. doi: 10.1145/3240508.3240583

Mining geo-social networks - spatial item recommendation

Yin, Hongzhi and Wang, Weiqing (2018). Mining geo-social networks - spatial item recommendation. 29th Australasian Database Conference (ADC), Gold Coast, Australia, 24-27 May 2018. Cham, Switzerland: Springer.

Mining subgraphs from propagation networks through temporal dynamic analysis

Hosseini, Saeid, Yin, Hongzhi, Zhang, Meihui, Elovici, Yuval and Zhou, Xiaofang (2018). Mining subgraphs from propagation networks through temporal dynamic analysis. 19th IEEE International Conference on Mobile Data Management, MDM 2018, Aalborg University, Aalborg, Denmark, 26-28 June 2018. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/MDM.2018.00023

Modeling patient visit using electronic medical records for cost profile estimation

Zhao, Kangzhi, Zhang, Yong, Wang, Zihao, Yin, Hongzhi, Zhou, Xiaofang, Wang, Jin and Xing, Chunxiao (2018). Modeling patient visit using electronic medical records for cost profile estimation. 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, Gold Coast, QLD, Australia, 21-24 May 2018. Heidelberg, Germany: Springer Verlag. doi: 10.1007/978-3-319-91458-9_2

Neural memory streaming recommender networks with adversarial training

Wang, Qinyong, Lian, Defu, Yin, Hongzhi, Wang, Hao, Hu, Zhiting and Huang, Zi (2018). Neural memory streaming recommender networks with adversarial training. 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018, London, United Kingdom, 19 - 23 August 2018. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3219819.3220004

PME: projected metric embedding on heterogeneous networks for link prediction

Chen, Hongxu, Wang, Hao, Yin, Hongzhi, Nguyen, Quoc Viet Hung, Wang, Weiqing and Li, Xue (2018). PME: projected metric embedding on heterogeneous networks for link prediction. 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018, London, United Kingdom, 19 - 23 August 2018. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3219819.3219986

Publishing graph node strength histogram with edge differential privacy

Qian, Qing, Li, Zhixu, Zhao, Pengpeng, Chen, Wei, Yin, Hongzhi and Zhao, Lei (2018). Publishing graph node strength histogram with edge differential privacy. 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, Gold Coast, QLD, Australia, 21-24 May 2018. Heidelberg, Germany: Springer Verlag. doi: 10.1007/978-3-319-91458-9_5

Restricted boltzmann machine based active learning for sparse recommendation

Wang, Weiqing, Yin, Hongzhi, Huang, Zi, Sun, Xiaoshuai and Hung, Nguyen Quoc Viet (2018). Restricted boltzmann machine based active learning for sparse recommendation. 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, Gold Coast, QLD Australia, 21 - 24 May 2018. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-91452-7_7

Stock assistant: a stock AI assistant for reliability modeling of stock comments

Zhang, Chen, Du, Changying, Wang, Yijun, Yin, Hongzhi, Chen, Can and Wang, Hao (2018). Stock assistant: a stock AI assistant for reliability modeling of stock comments. 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018, London, United Kingdom, 19 - 23 August 2018. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3219819.3219964

Streaming ranking based recommender systems

Wang, Weiqing, Yin, Hongzhi, Huang, Zi, Wang, Qinyong, Du, Xingzhong and Nguyen, Quoc Viet Hung (2018). Streaming ranking based recommender systems. 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, United States, 8-12 July 2018. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3209978.3210016

TADA: trend alignment with dual-attention multi-task recurrent neural networks for sales prediction

Chen, Tong, Yin, Hongzhi, Chen, Hongxu, Wu, Lin, Wang, Hao, Zhou, Xiaofang and Li, Xue (2018). TADA: trend alignment with dual-attention multi-task recurrent neural networks for sales prediction. 18th IEEE International Conference on Data Mining, ICDM 2018, Singapore, 17-20 November 2018. Piscataway, NJ USA: Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/ICDM.2018.00020

TSAUB: a temporal-sentiment-aware user behavior model for personalized recommendation

Wang, Qinyong, Yin, Hongzhi, Wang, Hao and Huang, Zi (2018). TSAUB: a temporal-sentiment-aware user behavior model for personalized recommendation. 29th Australasian Database Conference, ADC 2018, Gold Coast, QLD, Australia, 24-27 May 2018. Heidelberg, Germany: Springer Verlag. doi: 10.1007/978-3-319-92013-9_17

Towards the Learning of Weighted Multi-label Associative Classifiers

Liu, Chunyang, Chen, Ling, Tsang, Ivor and Yin, Hongzhi (2018). Towards the Learning of Weighted Multi-label Associative Classifiers. 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, July 8 - 13, 2018. Institute of Electrical and Electronics Engineers. doi: 10.1109/IJCNN.2018.8489398

Unified user and item representation learning for joint recommendation in social network

Yang, Jiali, Li, Zhixu, Yin, Hongzhi, Zhao, Pengpeng, Liu, An, Chen, Zhigang and Zhao, Lei (2018). Unified user and item representation learning for joint recommendation in social network. 19th International Conference on Web Information Systems Engineering, WISE 2018, Dubai, United Arab Emirates, November 12 - 15, 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-02925-8_3

User guidance for efficient fact checking

Nguyen, Thanh Tam, Weidlich, Matthias, Yin, Hongzhi, Zheng, Bolong, Hung Nguyen, Quoc Viet and Stantic, Bela (2018). User guidance for efficient fact checking. 45th International Conference on Very Large Data Bases, Los Angeles, CA United States, 2019. New York, NY United States: Association for Computing Machinery. doi: 10.14778/3324301.3324303

What-If analysis with conflicting goals: recommending data ranges for exploration

Nguyen, Quoc Viet Hung, Zheng, Kai, Weidlich, Matthias, Zheng, Bolong, Yin, Hongzhi, Nguyen, Thanh Tam and Stantic, Bela (2018). What-If analysis with conflicting goals: recommending data ranges for exploration. 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, April 16 - 19, 2018. Los Alamitos, CA, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDE.2018.00018

A location-sentiment-aware recommender system for both home-town and out-of-town users

Wang, Hao , Fu, Yanmei , Wang, Qinyong , Yin, Hongzhi , Du, Changying and Xiong, Hui (2017). A location-sentiment-aware recommender system for both home-town and out-of-town users. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Halifax, NS, Canada, 13-17 August 2017. New York, NY, United States: ACM. doi: 10.1145/3097983.3098122

A time and sentiment unification model for personalized recommendation

Wang, Qinyong , Yin, Hongzhi and Wang, Hao (2017). A time and sentiment unification model for personalized recommendation. Joint Conference, APWeb-WAIM, Beijing, China, 7-9 July 2017. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-63564-4 8

An integrated model for effective saliency prediction

Sun, Xiaoshuai, Huang, Zi, Yin, Hongzhi and Shen, Heng Tao (2017). An integrated model for effective saliency prediction. AAAI Conference on Artificial Intelligence, San Francisco, CA, United States, 4-9 February 2017. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence.

An integrated model for effective saliency prediction

Sun, Xiaoshuai, Huang, Zi, Yin, Hongzhi and Shen, Heng Tao (2017). An integrated model for effective saliency prediction. 31st AAAI Conference on Artificial Intelligence, AAAI 2017, San Francisco, CA., United States, 04-10 February 2017. Palo Alto, CA., United States: AAAI press.

Exploiting spatio-temporal user behaviors for user linkage

Chen, Wei, Yin, Hongzhi, Wang, Weiqing, Zhao, Lei, Hua, Wen and Zhou, Xiaofang (2017). Exploiting spatio-temporal user behaviors for user linkage. 26th ACM International Conference on Information and Knowledge Management, CIKM 2017, Singapore, Singapore, 06 - 10 November 2017. New York, New York, United States: Association for Computing Machinery. doi: 10.1145/3132847.3132898

Graph-based metric embedding for next POI recommendation

Xie, Min, Yin, Hongzhi, Xu, Fanjiang, Wang, Hao and Zhou, Xiaofang (2017). Graph-based metric embedding for next POI recommendation. 17th International Conference on Web Information Systems Engineering (WISE), Shanghai, China, 8 - 10 November 2016. Heidelberg, Germany: Springer . doi: 10.1007/978-3-319-48743-4_17

Group recommender model based on preference interaction

Zheng, Wei, Li, Bohan, Wang, Yanan, Yin, Hongzhi, Li, Xue, Guan, Donghai and Qin, Xiaolin (2017). Group recommender model based on preference interaction. 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, Singapore, Singapore, 5–6 November 2017. Heidelberg, Germany: Springer Verlag. doi: 10.1007/978-3-319-69179-4_10

Influenced nodes discovery in temporal contact network

Huang, Jinjing, Lin, Tianqiao, Liu, An, Li, Zhixu, Yin, Hongzhi and Zhao, Lei (2017). Influenced nodes discovery in temporal contact network. 18th International Conference on Web Information Systems Engineering, WISE 2017, Puschino, Russia, 7-11 October 2017. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-68783-4_32

Jointly modeling heterogeneous temporal properties in location recommendation

Hosseini, Saeid, Yin, Hongzhi, Zhang, Meihui, Zhou, Xiaofang and Sadiq, Shazia (2017). Jointly modeling heterogeneous temporal properties in location recommendation. 22nd Internation Conference, DASFAA 2017, Suzhou, China, 27 - 30 March 2017. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-55753-3_31

Mobi-SAGE: A sparse additive generative model for mobile app recommendation

Yin, Hongzhi, Chen, Liang, Wang, Weiqing, Du, Xingzhong, Nguyen, Quoc Viet Hung and Zhou, Xiaofang (2017). Mobi-SAGE: A sparse additive generative model for mobile app recommendation. IEEE 33rd International Conference on Data Engineering (ICDE), San Diego, CA, United States, 19-22 April 2017. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDE.2017.43

People opinion topic model: opinion based user clustering in social networks

Chen, Hongxu, Yin, Hongzhi, Li, Xue, Wang, Meng, Chen, Weitong and Chen, Tong (2017). People opinion topic model: opinion based user clustering in social networks. International Conference on World Wide Web Companion, Perth, Australia, 3-7 April 2017. Geneva, Switzerland: International World Wide Web Conferences Steering Committee. doi: 10.1145/3041021.3051159

Recommendation in context-rich environment: An information network analysis approach

Sun, Yizhou, Yin, Hongzhi and Ren, Xiang (2017). Recommendation in context-rich environment: An information network analysis approach. 26th International World Wide Web Conference, WWW 2017 Companion, Perth, WA, Australia, April 3 - 7, 2017. Geneva, Switzerland: International World Wide Web Conferences Steering Committee. doi: 10.1145/3041021.3051105

Retaining data from streams of social platforms with minimal regret

Tam, Nguyen Thanh, Weidlich, Matthias, Thang, Duong Chi, Yin, Hongzhi and Hung, Nguyen Quoc Viet (2017). Retaining data from streams of social platforms with minimal regret. International Joint Conference on Artificial Intelligence, IJCAI, Melbourne, Australia, 19-25 August 2017. Melbourne, Australia: International Joint Conferences on Artificial Intelligence. doi: 10.24963/ijcai.2017/397

SPTF: A scalable probabilistic tensor factorization model for semantic-aware behavior prediction

Yin, Hongzhi, Chen, Hongxu, Sun, Xiaoshuai, Wang, Hao, Wang, Yang and Nguyen, Quoc Viet Hung (2017). SPTF: A scalable probabilistic tensor factorization model for semantic-aware behavior prediction. 17th IEEE International Conference on Data Mining, ICDM 2017, New Orleans, LA, USA, November 18-21, 2017. New York, USA: Institute of Electrical and Electronics Engineers . doi: 10.1109/ICDM.2017.68

Time-constrained graph pattern matching in a large temporal graph

Xu, Yanxia, Huang, Jinjing, Liu, An, Li, Zhixu, Yin, Hongzhi and Zhao, Lei (2017). Time-constrained graph pattern matching in a large temporal graph. Joint Conference, APWeb-WAIM, Beijing, China, 7-9 July 2017. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-63579-8 9

Understanding the user display names across social networks

Li, Yongjun, Peng, You, Zhang, Zhen, Xu, Quanqing and Yin, Hongzhi (2017). Understanding the user display names across social networks. International Conference on World Wide Web Companion, Perth, Australia, 3-7 April 2017. Geneva, Switzerland: International World Wide Web Conferences Steering Committee. doi: 10.1145/3041021.3051146

A unified framework for fine-grained opinion mining from online reviews

Wang, Hao, Zhang, Chen, Yin, Hongzhi, Wang, Wei, Zhang, Jun and Xu, Fanjiang (2016). A unified framework for fine-grained opinion mining from online reviews. 49th Annual Hawaii International Conference on System Sciences, HICSS 2016, Koloa, HI, 5-8 January 2016. Piscataway, NJ, United States: I E E E. doi: 10.1109/HICSS.2016.144

Discovering interpretable geo-social communities for user behavior prediction

Yin, Hongzhi, Hu, Zhiting, Zhou, Xiaofang, Wang, Hao, Zheng, Kai, Quoc Viet Hung Nguyen and Sadiq, Shazia (2016). Discovering interpretable geo-social communities for user behavior prediction. IEEE International Conference on Data Engineering (ICDE), Helsinki, Finland, 16-20 May 2016. Washington, DC United States: IEEE Computer Society. doi: 10.1109/ICDE.2016.7498303

Expert team finding for review assignment

Yin, Hongzhi, Cui, Bin, Lu, Hua and Zhao, Lei (2016). Expert team finding for review assignment. Conference on Technologies and Applications of Artificial Intelligence (TAAI), Hsinchu, Taiwan, 25-27 November 2016. Piscataway, NJ, United States: IEEE. doi: 10.1109/TAAI.2016.7932314

Keyword-aware continuous kNN query on road networks

Zheng, Bolong, Zheng, Kai, Xiao, Xiaokui, Su, Han, Yin, Hongzhi, Zhou, Xiaofang and Li, Guohui (2016). Keyword-aware continuous kNN query on road networks. IEEE International Conference on Data Engineering (ICDE), Helsinki, Finland, 16-20 May 2016. Washington, DC United States: IEEE Computer Society. doi: 10.1109/ICDE.2016.7498297

LSIF: a system for large-scale information flow detection based on topic-related semantic similarity measurement

Zhao, Meng, Wang, Hao, Cao, Liangliang, Zhang, Chen, Yin, Hongzhi and Xu, Fanjiang (2016). LSIF: a system for large-scale information flow detection based on topic-related semantic similarity measurement. 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Singapore, 6-9 December 2016. Los Alamitos, CA United States: IEEE Computer Society. doi: 10.1109/WI-IAT.2015.2

Learning graph-based POI embedding for location-based recommendation

Xie, Min, Yin, Hongzhi, Wang, Hao, Xu, Fanjiang, Chen, Weitong and Wang, Sen (2016). Learning graph-based POI embedding for location-based recommendation. 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, United States, 24 - 28 October 2016. New York, NY, United States: ACM. doi: 10.1145/2983323.2983711

SPORE: a sequential personalized spatial item recommender system

Wang, Weiqing, Yin, Hongzhi, Sadiq, Shazia, Chen, Ling, Xie, Min and Zhou, Xiaofang (2016). SPORE: a sequential personalized spatial item recommender system. IEEE International Conference on Data Engineering (ICDE), Helsinki, Finland, 16-20 May 2016. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/ICDE.2016.7498304

Using detected visual objects to index video database

Du, Xingzhong, Yin, Hongzhi, Huang, Zi, Yang, Yi and Zhou, Xiaofang (2016). Using detected visual objects to index video database. Australasian Database Conference on Databases Theory and Applications, Sydney, Australia, 28-29 September 2016. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-46922-5_26

Discovering Organized POI Groups in a city

Xu, Yanxia, Liu, Guanfeng, Yin, Hongzhi, Xu, Jiajie, Zheng, Kai and Zhao, Lei (2015). Discovering Organized POI Groups in a city. 20th International Conference on Database Systems for Advanced Applications (DASFAA), Hanoi, Vietnam, 20-23 April 2015. Heidelberg, Germany: Springer International Publishing. doi: 10.1007/978-3-319-22324-7_19

Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion

Xie, Yiran, Yin, Hongzhi, Cui, Bin, Yao, Junjie and Xu, Quanqing (2015). Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion. 31st IEEE International Conference on Data Engineering Workshops 2015, Seoul, South Korea, 13-17 April 2015. IEEE Computer Society. doi: 10.1109/ICDEW.2015.7129573

Geo-SAGE: a geographical sparse additive generative model for spatial item recommendation

Wang, Weiqing, Yin, Hongzhi, Chen, Ling, Sun, Yizhou, Sadiq, Shazia and Zhou, Xiaofang (2015). Geo-SAGE: a geographical sparse additive generative model for spatial item recommendation. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 10-13 August 2015. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/2783258.2783335

Geographical constraint and temporal similarity modeling for point-of-interest recommendation

Wu, Huimin, Shao, Jie, Yin, Hongzhi, Shen, Heng Tao and Zhou, Xiaofang (2015). Geographical constraint and temporal similarity modeling for point-of-interest recommendation. International Conference on Web Information Systems Engineering, Miami, FL, United States, 1-3 November 2015. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-26187-4_40

Joint modeling of user check-in behaviors for point-of-interest recommendation

Yin, Hongzhi, Zhou, Xiaofang, Shao, Yingxia, Wang, Hao and Sadiq, Shazia (2015). Joint modeling of user check-in behaviors for point-of-interest recommendation. 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourme, VIC, Australia, 19-23 October, 2015. New York , NY, United States: Association for Computing Machinery. doi: 10.1145/2806416.2806500

Joint modeling of users' interests and mobility patterns for point-of-interest recommendation

Yin, Hongzhi, Cui, Bin, Huang, Zi, Wang, Weiqing, Wu, Xian and Zhou, Xiaofang (2015). Joint modeling of users' interests and mobility patterns for point-of-interest recommendation. 23rd ACM International Conference on Multimedia, MM 2015, Brisbane, QLD, Australia, 26-30 October, 2015. New York, NY, United States: Association for Computing Machinery, Inc. doi: 10.1145/2733373.2806339

Predicting passengers in public transportation using smart card data

Dou, Mengyu, He, Tieke, Yin, Hongzhi, Zhou, Xiaofang, Chen, Zhenyu and Luo, Bin (2015). Predicting passengers in public transportation using smart card data. 26th Australasian Database Conference (ADC), Melbourne Australia, 4-7 June 2015. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-19548-3_3

Predicting users' purchasing behaviors using their browsing history

He, Tieke, Yin, Hongzhi, Chen, Zhenyu, Zhou, Xiaofang and Luo, Bin (2015). Predicting users' purchasing behaviors using their browsing history. 26th Australasian Database Conference (ADC), Melbourne, Australia, 4-7 June 2015. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-19548-3_11

A temporal context-aware model for user behavior modeling in social media systems

Yin, Hongzhi, Cui, Bin, Chen, Ling, Hu, Zhiting and Huang, Zi (2014). A temporal context-aware model for user behavior modeling in social media systems. 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014, Snowbird, UT United States, 22-27 June 2014. New York, NY United States: Association for Computing Machinery. doi: 10.1145/2588555.2593685

A unified model for stable and temporal topic detection from social media data

Yin, Hongzhi, Cui, Bin, Lu, Hua, Huang, Yuxin and Yao, Junjie (2013). A unified model for stable and temporal topic detection from social media data. International Conference on Data Engineering, Brisbane, Australia, 8-11 April 2013. Washington, DC, United States: I E E E Computer Society. doi: 10.1109/ICDE.2013.6544864

LCARS: a location-content-aware recommender system

Yin, Hongzhi, Sun, Yizhou, Cui, Bin, Hu, Zhiting and Chen, Ling (2013). LCARS: a location-content-aware recommender system. 19th ACM SIGKDD Knowledge Discovery and Data Mining, Chicago, IL, United States, 11-14 August 2013. New York, NY, United States: ACM. doi: 10.1145/2487575.2487608

TeRec: a temporal recommender system over tweet stream

Chen, Chen, Yin, Hongzhi, Yao, Junjie and Cui, Bin (2013). TeRec: a temporal recommender system over tweet stream. VLDB2013: 39th International Conference on Very Large Data Bases, Riva del Garda, Trento, Italy, 26-30 August, 2013. New York, NY, USA: Association for Computing Machinery. doi: 10.14778/2536274.2536289

Challenging the long tail recommendation

Yin, Hongzhi, Cui, Bin, Li, Jing, Yao, Junjie and Chen, Chen (2012). Challenging the long tail recommendation. 38th International Conference on Very Large Data Bases 2012, (VLDB 2012), Istanbul, Turkey, 27-31 August 2012. New York, NY United States: Association for Computing Machinery. doi: 10.14778/2311906.2311916

Finding a wise group of experts in social networks

Yin, Hongzhi, Cui, Bin and Huang, Yuxin (2011). Finding a wise group of experts in social networks. ADMA 2011: 7th International Conference on Advanced Data Mining and Applications, Beijing, China, 17-19 December, 2011. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-25853-4_29

Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17-20, 2023, Proceedings, Part I

Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao and Hongzhi Yin eds. (2023). Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17-20, 2023, Proceedings, Part I. 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), Tianjin, China, 17-20 April 2023. Heidelberg, Germany: Springer.

Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II

Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao and Hongzhi Yin eds. (2023). Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II. 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), Tianjin, China, 17-20 April 2023. Heidelberg, Germany: Springer.

Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part IV

Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao and Hongzhi Yin eds. (2023). Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part IV. 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), Tianjin, China, 17-20 April 2023. Heidelberg, Germany: Springer.

Database Systems for Advanced Applications : 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part III

Wang, Xin, Sapino, Maria Luisa, Han, Wook-Shin, El Abbadi, Amr, Dobbie, Gill, Feng, Zhiyong, Shao, Yingxiao and Yin, Hongzhi eds. (2023). Database Systems for Advanced Applications : 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part III. 28th International Conference, DASFAA 2023, Tianjin, China, 17–20 April 2023. Cham, Switzerland: Springer.

Database systems for advanced applications:  28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part III

Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao and Hongzhi Yin eds. (2023). Database systems for advanced applications:  28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part III. 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), Tianjin, China, 17-20 April 2023. Heidelberg, Germany: Springer . doi: 10.1007/978-3-031-30675-4