The University of Queensland

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

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

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

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

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

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

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

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

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

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

Integrating crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning

Chen, Qiaomin, Zheng, Bangyou, Chen, Tong and Chapman, Scott C. (2022). Integrating crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning. Journal of Experimental Botany, 73 (19), 6558-6574. doi: 10.1093/jxb/erac291

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

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

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

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

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

Attribute-aware explainable complementary clothing recommendation

Li, Yang, Chen, Tong and Huang, Zi (2021). Attribute-aware explainable complementary clothing recommendation. World Wide Web, 24 (5), 1885-1901. doi: 10.1007/s11280-021-00913-3

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

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

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

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

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

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

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

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

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

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

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.

Integrating APSIM and PROSAIL to improve prediction of crop traits in various situations from hyperspectral data using deep learning

Chen, Qiaomin, Zheng, Bangyou, Chen, Tong and Chapman, Scott (2022). Integrating APSIM and PROSAIL to improve prediction of crop traits in various situations from hyperspectral data using deep learning. 20th Agronomy Australia Conference, Toowoomba, QLD, Australia, 18-22 September 2022. Willow Grove, VIC Australia: Australian Society of Agronomy.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Minimal path based particle tracking in low SNR fluorescence microscopy images

Lu, Sheng, Chen, Tong, Yang, Fan, Peng, Chenglei, Du, Sidan and Li, Yang (2019). Minimal path based particle tracking in low SNR fluorescence microscopy images. Association for Computing Machinery. doi: 10.1145/3354031.3354035

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

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

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

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

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

Rumor detection via recurrent neural networks: a case study on adaptivity with varied data compositions

Chen, Tong, Chen, Hongxu and Li, Xue (2018). Rumor detection via recurrent neural networks: a case study on adaptivity with varied data compositions. 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_10

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

Generating life course trajectory sequences with recurrent neural networks and application to early detection of social disadvantage

Wu, Lin, Haynes, Michele, Smith, Andrew, Chen, Tong and Li, Xue (2017). Generating life course trajectory sequences with recurrent neural networks and application to early detection of social disadvantage. Advanced Data Mining and Applications 13th International Conference, Singapore, November 5–6, 2017. Heidelberg, Germany: Springer . doi: 10.1007/978-3-319-69179-4_16

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

Sequence modelling for e-commerce

Chen, Tong (2020). Sequence modelling for e-commerce. PhD Thesis, School of Information Technology and Electrical Engineering, The University of Queensland. doi: 10.14264/uql.2020.1003