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Professor Shane Culpepper

Professor, Artificial Intelligence

School of Electrical Engineering and Computer Science
Faculty of Engineering, Architecture and Information Technology
s.culpepper@uq.edu.au

Overview

Shane Culpepper is Professor of Artificial Intelligence at the University of Queensland in St. Lucia, Australia. Before joining the University of Queensland in 2023, Professor Culpeper held a continuing academic position at RMIT University in Melbourne, Australia. He received his PhD in Computer Science from the University of Melbourn in 2008. His research focuses primarily on building better Search and Recommendation Systems. Over his 16 year career, Professor Culpepper has supervised 19 PhD students and co-authored more than 120 peer reviewed papers with 127 different research collaborators on problems such as algorithm efficiency and scalability, new machine learning algorithms for search and recommendation systems, and evaluating search and recommendation engine quality. Professor Culpepper is also an active member in the international research community. In the last 5 years, he has been a program co-chair for international conferences such as SIGIR and CIKM, and co-organized conferences such as WSDM and SWIRL. Professor Culpepper previously held an ARC DECRA fellowship in 2013 as well as an RMIT Vice-Chancellor's Princpal Researcher fellowship in 2017. Before joining the University of Queensland. Professor Culpepper was the founding director of the Centre for Information Discovery and Data Analytics at RMIT University. In total, he has been a chief investigator on 11 reseach grants totalling ~$3.5 Million AUD. For more information, see his personal hoomepage.

Qualifications

  • Honorary Fellow, RMIT University, RMIT University
  • Member, Association for Computing Machinery, Association for Computing Machinery
  • Doctor of Philosophy of Computer Science, University of Melbourne

Publications

View all Publications

Available Projects

  • Large Language Models for Search and Recommendation

    Large Language Models such as ChatGPT offer enormous promise to users completing everyday tasks. However, these models confidently provide misinformation which can be very convincing. This project aims to explore new techniques to improve the effectiveness of LLMs.

View all Available Projects

Publications

Book Chapter

  • An Enhanced Evaluation Framework for Query Performance Prediction

    Faggioli, Guglielmo, Zendel, Oleg, Culpepper, J. Shane, Ferro, Nicola and Scholer, Falk (2021). An Enhanced Evaluation Framework for Query Performance Prediction. Lecture Notes in Computer Science. (pp. 115-129) Cham: Springer International Publishing. doi: 10.1007/978-3-030-72113-8_8

  • Bayesian System Inference on Shallow Pools

    Benham, Rodger, Moffat, Alistair and Culpepper, J. Shane (2021). Bayesian System Inference on Shallow Pools. Lecture Notes in Computer Science. (pp. 209-215) Cham: Springer International Publishing. doi: 10.1007/978-3-030-72240-1_17

  • On the Pluses and Minuses of Risk

    Benham, Rodger, Moffat, Alistair and Culpepper, J. Shane (2020). On the Pluses and Minuses of Risk. Information Retrieval Technology. (pp. 81-93) Cham: Springer International Publishing. doi: 10.1007/978-3-030-42835-8_8

Journal Article

Conference Publication

  • Enhancing Human Annotation: Leveraging Large Language Models and Efficient Batch Processing

    Zendel, Oleg, Culpepper, J. Shane, Scholer, Falk and Thomas, Paul (2024). Enhancing Human Annotation: Leveraging Large Language Models and Efficient Batch Processing. New York, NY, USA: ACM. doi: 10.1145/3627508.3638322

  • Facility Relocation Search For Good: When Facility Exposure Meets User Convenience

    Luo, Hui, Bao, Zhifeng, Culpepper, J. Shane, Li, Mingzhao and Zhao, Yanchang (2023). Facility Relocation Search For Good: When Facility Exposure Meets User Convenience. New York, NY, USA: ACM. doi: 10.1145/3543507.3583859

  • Entropy-based query performance prediction for neural information retrieval systems

    Zendel, Oleg, Liu, Binsheng, Culpepper, J. Shane and Scholer, Falk (2023). Entropy-based query performance prediction for neural information retrieval systems. QPP++ 2023: Query Performance Prediction and Its Evaluation in New Tasks, co-located with The 45th European Conference on Information Retrieval (ECIR), Dublin, Ireland, 2 - 6 April 2023. CEUR-WS.

  • Representative routes discovery from massive trajectories

    Wang, Tingting, Huang, Shixun, Bao, Zhifeng, Culpepper, J. Shane and Arablouei, Reza (2022). Representative routes discovery from massive trajectories. KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, United States, 14 - 18 August 2022. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3534678.3539079

  • Can users predict relative query effectiveness?

    Zendel, Oleg, Ebrahim, Melika P., Culpepper, J. Shane, Moffat, Alistair and Scholer, Falk (2022). Can users predict relative query effectiveness?. 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Madrid, Spain, 11-15 July 2022. New York, NY USA: Assocation for Computing Machinery. doi: 10.1145/3477495.3531893

  • Different keystrokes for different folks: visualizing crowdworker querying behavior

    Benham, Rodger, MacKenzie, Joel, Culpepper, J. Shane and Moffat, Alistair (2021). Different keystrokes for different folks: visualizing crowdworker querying behavior. CHIIR '21: ACM SIGIR Conference on Human Information Interaction and Retrieval, Canberra, ACT Australia, 14 - 19 March 2021. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3406522.3446054

  • Do hard topics exist? A statistical analysis

    Culpepper, J. Shane, Faggioli, Guglielmo, Ferro, Nicola and Kurland, Oren (2021). Do hard topics exist? A statistical analysis. IIR 2021: 11th Italian Information Retrieval Workshop 2021, Bari, Italy, 13-15 September 2021. Aachen, Germany: Rheinisch-Westfaelische Technische Hochschule Aachen.

  • Generalizing discriminative retrieval models using generative tasks

    Liu, Binsheng, Zamani, Hamed, Lu, Xiaolu and Culpepper, J. Shane (2021). Generalizing discriminative retrieval models using generative tasks. 30th World Wide Web Conference (WWW), Virtual, 12-23 April 2021. New York, NY, United States: ACM. doi: 10.1145/3442381.3449863

  • Is query performance prediction with multiple query variations harder than topic performance prediction?

    Zendel, Oleg, Culpepper, J. Shane and Scholer, Falk (2021). Is query performance prediction with multiple query variations harder than topic performance prediction?. 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual, 11-15 July 2021. New York, NY, United States: ACM. doi: 10.1145/3404835.3463039

  • CC-News-En : A large English news corpus

    Mackenzie, Joel, Benham, Rodger, Petri, Matthias, Trippas, Johanne R., Culpepper, J. Shane and Moffat, Alistair (2020). CC-News-En : A large English news corpus. CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Online, 19 - 23 October 2020. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3340531.3412762

  • Feature extraction for large-scale text collections

    Gallagher, Luke, Mallia, Antonio, Culpepper, J. Shane, Suel, Torsten and Cambazoglu, B. Barla (2020). Feature extraction for large-scale text collections. 29th ACM International Conference on Information and Knowledge Management (CIKM), Virtual, 19-23 October 2020. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3340531.3412773

  • Cluster-based document retrieval with multiple queries

    Bernstein, Kfir, Raiber, Fiana, Kurland, Oren and Shane Culpepper, J. (2020). Cluster-based document retrieval with multiple queries. ICTIR '20: The 2020 ACM SIGIR International Conference on the Theory of Information Retrieval, Virtual, 14-17 September 2020. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3409256.3409825

  • Bayesian inferential risk evaluation on multiple IR systems

    Benham, Rodger, Carterette, Ben, Culpepper, J. Shane and Moffat, Alistair (2020). Bayesian inferential risk evaluation on multiple IR systems. 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Virtual, 25-30 July 2020. New York, NY, United States: ACM. doi: 10.1145/3397271.3401033

  • Spatial object recommendation with hints: when spatial granularity matters

    Luo, Hui, Zhou, Jingbo, Bao, Zhifeng, Li, Shuangli, Culpepper, J. Shane, Ying, Haochao, Liu, Hao and Xiong, Hui (2020). Spatial object recommendation with hints: when spatial granularity matters. 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Virtual, 25-30 July 2020. New York, NY, United States: ACM. doi: 10.1145/3397271.3401090

  • Temporal network representation learning via historical neighborhoods aggregation

    Huang, Shixun, Bao, Zhifeng, Li, Guoliang, Zhou, Yanghao and Culpepper, J. Shane (2020). Temporal network representation learning via historical neighborhoods aggregation. IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, United States, 20-24 April 2020. Los Alamitos, CA, United States: IEEE Computer Society. doi: 10.1109/icde48307.2020.00101

  • A comparative analysis of human and automatic query variants

    Liu, Binsheng, Craswell, Nick, Lu, Xiaolu, Kurland, Oren and Culpepper, J. Shane (2019). A comparative analysis of human and automatic query variants. ACM SIGIR 9th International Conference on the Theory of Information Retrieval (ICTIR), Santa Clara, CA USA, 2-5 October 2019. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3341981.3344223

  • Relevance modeling with multiple query variations

    Lu, Xiaolu, Kurland, Oren, Culpepper, J. Shane, Craswell, Nick and Rom, Ofri (2019). Relevance modeling with multiple query variations. ACM SIGIR 9th International Conference on the Theory of Information Retrieval (ICTIR), Santa Clara, CA USA, 2-5 October 2019. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3341981.3344224

  • Information needs, queries, and query performance prediction

    Zendel, Oleg, Shtok, Anna, Raiber, Fiana, Kurland, Oren and Culpepper, J. Shane (2019). Information needs, queries, and query performance prediction. 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France, 21-25 July 2019. New York, NY, United States: ACM. doi: 10.1145/3331184.3331253

  • Accelerated query processing via similarity score prediction

    Petri, Matthias, Moffat, Alistair, Mackenzie, Joel, Culpepper, J. Shane and Beck, Daniel (2019). Accelerated query processing via similarity score prediction. 42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Paris, France, 21-25 July 2019. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3331184.3331207

  • Compressing inverted indexes with recursive graph bisection: A reproducibility study

    Mackenzie, Joel, Mallia, Antonio, Petri, Matthias, Culpepper, J. Shane and Suel, Torsten (2019). Compressing inverted indexes with recursive graph bisection: A reproducibility study. 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, 14-18 April 2019. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-15712-8_22

  • Finding temporal influential users over evolving social networks

    Huang, Shixun, Bao, Zhifeng, Culpepper, J. Shane and Zhang, Bang (2019). Finding temporal influential users over evolving social networks. IEEE 35th International Conference on Data Engineering (ICDE), Macau, China, 8 - 11 April 2019. Washington, DC, United States: I E E E Computer Society. doi: 10.1109/icde.2019.00043

  • Joint optimization of cascade ranking models

    Gallagher, Luke, Chen, Ruey-Cheng, Blanco, Roi and Culpepper, J. Shane (2019). Joint optimization of cascade ranking models. WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, Melbourne, VIC, Australia, 11 - 15 February 2019. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3289600.3290986

  • On topic difficulty in IR evaluation: the effect of systems, corpora, and system components

    Zampieri, Fabio, Roitero, Kevin, Culpepper, J. Shane, Kurland, Oren and Mizzaro, Stefano (2019). On topic difficulty in IR evaluation: the effect of systems, corpora, and system components. 42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Paris, France, 21-25 July 2019. New York, NY, United States: ACM. doi: 10.1145/3331184.3331279

  • Taking Risks with Confidence

    Benham, Rodger, Carterette, Ben, Moffat, Alistair and Culpepper, J. Shane (2019). Taking Risks with Confidence. 24th Australasian Document Computing Symposium (ADCS), Sydney Australia, Dec 05-06, 2019. NEW YORK: ASSOC COMPUTING MACHINERY. doi: 10.1145/3372124.3372125

  • The Potential of Learned Index Structures for Index Compression

    Oosterhuis, Harrie, Culpepper, J. Shane and de Rijke, Maarten (2018). The Potential of Learned Index Structures for Index Compression. 23rd Australasian Document Computing Symposium (ADCS), Dunedin, New Zealand, 11-12 December 2018. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3291992.3291993

  • Revisiting spam filtering in web search

    Gallagher, Luke, Mackenzie, Joel and Culpepper, J. Shane (2018). Revisiting spam filtering in web search. 23rd Australasian Document Computing Symposium (ADCS), Dunedin, New Zealand, 11-12 December 2018. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3291992.3291999

  • Presentation ordering eects on assessor agreement

    Damessie, Tadele T., Kim, Jaewon, Shane Culpepper, J. and Scholer, Falk (2018). Presentation ordering eects on assessor agreement. Association for Computing Machinery. doi: 10.1145/3269206.3271750

  • On the cost of negation for dynamic pruning

    Mackenzie, Joel, Macdonald, Craig, Scholer, Falk and Culpepper, J. Shane (2018). On the cost of negation for dynamic pruning. 40th European Conference on Information Retrieval Research (ECIR), Grenoble, France, 26-29 March 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-76941-7_42

  • Query driven algorithm selection in early stage retrieval

    Mackenzie, Joel, Culpepper, J. Shane, Blanco, Roi, Crane, Matt, Clarke, Charles L. A. and Lin, Jimmy (2018). Query driven algorithm selection in early stage retrieval. 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA United States, 5-9 February 2018. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3159652.3159676

  • Dynamic Shard Cutoff Prediction for Selective Search

    Mohammad, Hafeezul Rahman, Xu, Keyang, Callan, Jamie and Culpepper, J. Shane (2018). Dynamic Shard Cutoff Prediction for Selective Search. 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Ann Arbor, MI United States, 8-12 July 2018. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3209978.3210005

  • Fusion in Information Retrieval

    Kurland, Oren and Culpepper, J. Shane (2018). Fusion in Information Retrieval. 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Ann Arbor Mi, Jul 08-12, 2018. NEW YORK: ASSOC COMPUTING MACHINERY. doi: 10.1145/3209978.3210186

  • Geo-social Influence Spanning Maximization

    Li, Jianxin, Sellis, Timos, Culpepper, J. Shane, He, Zhenying, Liu, Chengfei and Wang, Junhu (2018). Geo-social Influence Spanning Maximization. 34th IEEE International Conference on Data Engineering Workshops (ICDEW), Paris France, Apr 16-19, 2018. NEW YORK: IEEE. doi: 10.1109/ICDE.2018.00245

  • Improving Search Effectiveness with Field-based Relevance Modeling

    Liu, Binsheng, Lu, Xiaolu, Kurland, Oren and Culpepper, J. Shane (2018). Improving Search Effectiveness with Field-based Relevance Modeling. 23rd Australasian Document Computing Symposium (ADCS), Dunedin New Zealand, Dec 11-12, 2018. NEW YORK: ASSOC COMPUTING MACHINERY. doi: 10.1145/3291992.3292005

  • MaxBR<i>k</i>NN Queries for Streaming Geo-Data

    Luo, Hui, Choudhury, Farhana M., Bao, Zhifeng, Culpepper, J. Shane and Zhang, Bang (2018). MaxBRkNN Queries for Streaming Geo-Data. 23rd International Conference on Database Systems for Advanced Applications (DASFAA)., Gold Coast Australia, May 21-24, 2018. CHAM: SPRINGER INTERNATIONAL PUBLISHING AG. doi: 10.1007/978-3-319-91452-7_42

  • Neural Query Performance Prediction using Weak Supervision from Multiple Signals

    Zamani, Hamed, Croft, W. Bruce and Culpepper, J. Shane (2018). Neural Query Performance Prediction using Weak Supervision from Multiple Signals. 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Ann Arbor Mi, Jul 08-12, 2018. NEW YORK: ASSOC COMPUTING MACHINERY. doi: 10.1145/3209978.3210041

  • Reverse k Nearest Neighbor Search over Trajectories (Extended Abstract)

    Wang, Sheng, Bao, Zhifeng, Culpepper, J. Shane, Sellis, Timos and Cong, Gao (2018). Reverse k Nearest Neighbor Search over Trajectories (Extended Abstract). 34th IEEE International Conference on Data Engineering Workshops (ICDEW), Paris, France, 16-19 April 2018. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDE.2018.00250

  • Torch: A Search Engine for Trajectory Data

    Wang, Sheng, Bao, Zhifeng, Culpepper, J. Shane, Xie, Zizhe, Liu, Qizhi and Qin, Xiaolin (2018). Torch: A Search Engine for Trajectory Data. 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Ann Arbor Mi, Jul 08-12, 2018. NEW YORK: ASSOC COMPUTING MACHINERY. doi: 10.1145/3209978.3209989

  • Towards efficient and effective query variant generation

    Benham, Rodger, Culpepper, J. Shane, Gallagher, Luke, Lu, Xiaolu and Mackenzie, Joel (2018). Towards efficient and effective query variant generation. First Biennial Conference on Design of Experimental Search & Information Retrieval Systems (DESIRES 2018), Bertinoro, Italy, 28-31 August 2018. Aachen, Germany: RWTH Aachen University.

  • Early termination heuristics for score-at-a-time index traversal

    Mackenzie, Joel, Scholer, Falk and Culpepper, J. Shane (2017). Early termination heuristics for score-at-a-time index traversal. ADCS 2017: The 22nd Australasian Document Computing Symposium, Brisbane, QLD Australia, 7-8 December 2017. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3166072.3166073

  • Risk-reward trade-offs in rank fusion

    Benham, Rodger and Culpepper, J. Shane (2017). Risk-reward trade-offs in rank fusion. 22nd Australasian Document Computing Symposium, ADCS 2017, Brisbane, QLD Australia, 7 - 8 December 2017. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3166072.3166084

  • Answering top-k exemplar trajectory queries

    Wang, Sheng, Bao, Zhifeng, Culpepper, J. Shane, Sellis, Timos, Sanderson, Mark and Qin, Xiaolin (2017). Answering top-k exemplar trajectory queries. 2017 IEEE 33rd International Conference on Data Engineering (ICDE), San Diego, CA, United States, 19-22 April 2017. Piscataway, NJ United States: IEEE. doi: 10.1109/icde.2017.114

  • Monitoring the top-m rank aggregation of spatial objects in streaming queries

    Choudhury, Farhana M., Bao, Zhifeng, Culpepper, J. Shane and Sellis, Timos (2017). Monitoring the top-m rank aggregation of spatial objects in streaming queries. 2017 IEEE 33rd International Conference on Data Engineering (ICDE), San Diego, CA, United States, 19-22 April 2017. Piscataway, NJ United States: IEEE. doi: 10.1109/icde.2017.113

  • Personalized influential topic search via social network summarization

    Li, Jianxin, Liu, Chengfei, Yu, Jeffrey Xu, Chen, Yi, Sellis, Timos and Culpepper, J. Shane (2017). Personalized influential topic search via social network summarization. 2017 IEEE 33rd International Conference on Data Engineering (ICDE), San Diego, CA, United States, 19-22 April 2017. Piscataway, NJ United States: IEEE. doi: 10.1109/icde.2017.15

  • A comparison of document-at-a-time and score-at-a-time query evaluation

    Crane, Matt, Culpepper, J. Shane, Lin, Jimmy, Mackenzie, Joel and Trotman, Andrew (2017). A comparison of document-at-a-time and score-at-a-time query evaluation. 10th ACM International Conference on Web Search and Data Mining (WSDM), Cambridge, United Kingdom, 6-10 February 2017. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3018661.3018726

  • Can Deep Effectiveness Metrics Be Evaluated Using Shallow Judgment Pools?

    Lu, Xiaolu, Moffat, Alistair and Culpepper, J. Shane (2017). Can Deep Effectiveness Metrics Be Evaluated Using Shallow Judgment Pools?. 40th International ACM SIGIR conference on research and development in Information Retrieval, Shinjuku Japan, Aug 07-11, 2017. NEW YORK: ASSOC COMPUTING MACHINERY. doi: 10.1145/3077136.3080793

  • Efficient Cost-Aware Cascade Ranking in Multi-Stage Retrieval

    Chen, Ruey-Cheng, Gallagher, Luke, Blanco, Roi and Culpepper, J. Shane (2017). Efficient Cost-Aware Cascade Ranking in Multi-Stage Retrieval. 40th International ACM SIGIR conference on research and development in Information Retrieval, Shinjuku Japan, Aug 07-11, 2017. NEW YORK: ASSOC COMPUTING MACHINERY. doi: 10.1145/3077136.3080819

  • Gauging the Quality of Relevance Assessments using Inter-Rater Agreement

    Damessie, Tadele T., Nghiem, Thao P., Scholer, Falk and Culpepper, J. Shane (2017). Gauging the Quality of Relevance Assessments using Inter-Rater Agreement. 40th International ACM SIGIR conference on research and development in Information Retrieval, Shinjuku Japan, Aug 07-11, 2017. NEW YORK: ASSOC COMPUTING MACHINERY. doi: 10.1145/3077136.3080729

  • Dynamic cutoff prediction in multi-stage retrieval systems

    Culpepper, J. Shane, Clarke, Charles L. A. and Lin, Jimmy (2016). Dynamic cutoff prediction in multi-stage retrieval systems. 21st Australasian Document Computing Symposium, ADCS 2016, Caulfield, VIC Australia, 5 - 7 December 2016. New York, NY, United States: ACM. doi: 10.1145/3015022.3015026

  • Interactive trip planning using activity trajectories

    Wang, Sheng, Bao, Zhifeng, Culpepper, J. Shane, Sellis, Timos, Sanderson, Mark and Yadamjav, Munkh-Erdene (2016). Interactive trip planning using activity trajectories. 21st Australasian Document Computing Symposium, ADCS 2016, Caulfield, VIC Australia, 6 - 7 December 2016. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3015022.3015030

  • The influence of topic difficulty, relevance level, and document ordering on relevance judging

    Damessie, Tadele T., Scholer, Falk and Culpepper, J. Shane (2016). The influence of topic difficulty, relevance level, and document ordering on relevance judging. ADCS '16: 21st Australasian Document Computing Symposium, Caulfield, VIC Australia, 5 - 7 December 2016. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3015022.3015033

  • Modeling relevance as a function of retrieval rank

    Lu, Xiaolu, Moffat, Alistair and Culpepper, J. Shane (2016). Modeling relevance as a function of retrieval rank. 12th Asia Information Retrieval Societies Conference, AIRS 2016, Beijing, China, 30 November - 2 December 2016. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-319-48051-0_1

  • Spatial textual top-k search in mobile peer-to-peer networks

    Nghiem, Thao P., Ma, Cong, Culpepper, J. Shane and Sellis, Timos (2016). Spatial textual top-k search in mobile peer-to-peer networks. 27th Australasian Database Conference on Databases Theory and Applications, ADC 2016, Sydney, NSW Australia, 28-29 September 2016. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-46922-5_6

  • Efficient and effective higher order proximity modeling

    Lu, Xiaolu, Moffat, Alistair and Culpepper, J. Shane (2016). Efficient and effective higher order proximity modeling. 2016 ACM International Conference on the Theory of Information Retrieval, Newark, NJ United States, 12 - 16 September 2016. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/2970398.2970404

  • Maximizing bichromatic reverse spatial and textual k nearest neighbor queries

    Choudhury, Farhana M., Culpepper, J. Shane, Sellis, Timos and Cao, Xin (2016). Maximizing bichromatic reverse spatial and textual k nearest neighbor queries. 42nd International Conference on Very Large Data Bases, New Delhi, India, 9 September 2016. New York, NY United States: Association for Computing Machinery. doi: 10.14778/2904121.2904122

  • Load-balancing in distributed selective search

    Kim, Yubin, Callan, Jamie, Culpepper, J. Shane and Moffat, Alistair (2016). Load-balancing in distributed selective search. SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval, Pisa, Italy, 17 - 21 July 2016. New York, NY United States: Association for Computing Machinery. doi: 10.1145/2911451.2914689

  • Does selective search benefit from WAND optimization?

    Kim, Yubin, Callan, Jamie, Culpepper, J. Shane and Moffat, Alistair (2016). Does selective search benefit from WAND optimization?. 38th European Conference on IR Research, ECIR 2016, Padua, Italy, 20-23 March 2016. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-30671-1_11

  • RMIT at the TREC 2016 LiveQA Track

    Mackenzie, Joel, Chen, Ruey-Cheng and Culpepper, J. Shane (2016). RMIT at the TREC 2016 LiveQA Track. 25th Text REtrieval Conference, TREC 2016, Gaithersburg, MD United States, 15 November 2016. Gaithersburg, MD United States: National Institute of Standards and Technology.

  • Data fusion for Japanese term and character n-gram search

    Yasukawa, Michiko, Culpepper, J. Shane and Scholer, Falk (2015). Data fusion for Japanese term and character n-gram search. Association for Computing Machinery. doi: 10.1145/2838931.2838939

  • Efficient location-aware web search

    Mackenzie, Joel, Choudhury, Farhana M. and Culpepper, J. Shane (2015). Efficient location-aware web search. ADCS '15: The 20th Australasian Document Computing Symposium, Parramatta, NSW Australia, 8-9 December 2015. New York, NY United States: Association for Computing Machinery. doi: 10.1145/2838931.2838933

  • On the cost of extracting proximity features for term-dependency models

    Lu, Xiaolu, Moffat, Alistair and Culpepper, J. Shane (2015). On the cost of extracting proximity features for term-dependency models. Association for Computing Machinery. doi: 10.1145/2806416.2806467

  • Batch processing of Top-k Spatial-textual Queries

    Choudhury, Farhana M., Culpepper, J. Shane and Sellis, Timos (2015). Batch processing of Top-k Spatial-textual Queries. Association for Computing Machinery, Inc. doi: 10.1145/2786006.2786008

  • How effective are proximity scores in term dependency models?

    Lu, Xiaolu, Moffat, Alistair and Shane Culpepper, J. (2014). How effective are proximity scores in term dependency models?. Association for Computing Machinery. doi: 10.1145/2682862.2682876

  • Improving test collection pools with machine learning

    Jayasinghe, Gaya K., Webber, William, Sanderson, Mark and Shane Culpepper, J. (2014). Improving test collection pools with machine learning. Association for Computing Machinery. doi: 10.1145/2682862.2682864

  • Preface

    Culpepper, J. Shane, Park, Laurence and Zuccon, Guido (2014). Preface. ADCS '14: Australasian Document Computing Symposium, Melbourne, Australia, 27-28 November 2014. New York, United States: Association for Computing Machinery. doi: 10.1016/S2212-5671(14)00870-3

  • Evaluating non-deterministic retrieval systems

    Jayasinghe, Gaya K., Webber, William, Sanderson, Mark, Dharmasena, Lasitha S. and Culpepper, J. Shane (2014). Evaluating non-deterministic retrieval systems. Association for Computing Machinery. doi: 10.1145/2600428.2609472

  • Extending test collection pools without manual runs

    Jayasinghe, Gaya K., Webber, William, Sanderson, Mark and Culpepper, J. Shane (2014). Extending test collection pools without manual runs. Association for Computing Machinery. doi: 10.1145/2600428.2609473

  • Score-safe term dependency processing with hybrid indexes

    Petri, Matthias, Moffat, Alistair and Culpepper, J. Shane (2014). Score-safe term dependency processing with hybrid indexes. Association for Computing Machinery. doi: 10.1145/2600428.2609469

  • TREC: Topic engineeRing ExerCise

    Culpepper, J. Shane, Mizzaro, Stefano, Sanderson, Mark and Scholer, Falk (2014). TREC: Topic engineeRing ExerCise. Association for Computing Machinery. doi: 10.1145/2600428.2609531

  • Chairs' Preface

    Sitbon, Laurianne, Culpepper, Shane and Zuccon, Guido (2013). Chairs' Preface. ADCS '13: The Australasian Document Computing Symposium, Brisbane, QLD, Australia, 5-6 December 2013. New York, United States: Association for Computing Machinery.

  • Exploring the magic of WAND

    Petri, Matthias, Culpepper, J. Shane and Moffat, Alistair (2013). Exploring the magic of WAND. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/2537734.2537744

  • Sketch-based indexing of n-words

    Huston, Samuel, Culpepper, J. Shane and Croft, W. Bruce (2012). Sketch-based indexing of n-words. doi: 10.1145/2396761.2398533

  • Efficient indexing algorithms for approximate pattern matching in text

    Petri, Matthias and Culpepper, J. Shane (2012). Efficient indexing algorithms for approximate pattern matching in text. doi: 10.1145/2407085.2407087

  • Efficient in-memory top-k document retrieval

    Culpepper, J. Shane, Petri, Matthias and Scholer, Falk (2012). Efficient in-memory top-k document retrieval. doi: 10.1145/2348283.2348317

  • RMIT at TREC 2011 microblog track

    Petri, Matthias, Shane Culpepper, J. and Scholer, Falk (2011). RMIT at TREC 2011 microblog track.

  • Backwards search in context bound text transformations

    Petri, Matthias, Navarro, Gonzalo, Culpepper, J. Shane and Puglisi, Simon J. (2011). Backwards search in context bound text transformations. doi: 10.1109/CCP.2011.18

  • Top-k ranked document search in general text databases

    Culpepper, J. Shane, Navarro, Gonzalo, Puglisi, Simon J. and Turpin, Andrew (2010). Top-k ranked document search in general text databases. 18th European Symposium on Algorithms (ESA), Liverpool England, Sep 06-08, 2010. BERLIN: SPRINGER-VERLAG BERLIN. doi: 10.1007/978-3-642-15781-3_17

  • Including summaries in system evaluation

    Turpin, Andrew, Scholer, Falk, Jarvelin, Kalvero, Wu, Mingfang and Culpepper, J. Shane (2009). Including summaries in system evaluation. doi: 10.1145/1571941.1572029

  • Entropy of the retina template

    Arakala, A., Culpepper, J. S., Jeffers, J., Turpin, A., Boztaş, S., Horadam, K. J. and McKendrick, A. M. (2009). Entropy of the retina template. doi: 10.1007/978-3-642-01793-3_126

  • Compact set representation for information retrieval

    Culpepper, J. Shane and Moffat, Alistair (2007). Compact set representation for information retrieval. 14th International Symposium on String Processing and Information Retrieval, Santiago Chile, Oct 29-31, 2007. BERLIN: Springer Verlag. doi: 10.1007/978-3-540-75530-2_13

  • Phrase-based pattern matching in compressed text

    Culpepper, J. Shane and Moffat, Alistair (2006). Phrase-based pattern matching in compressed text. 13th International Conference on String Processing and Information Retrieval, SPIRE 2006, Glasgow, Scotland, 11-13 October 2006. Heidelberg, Germany: Springer. doi: 10.1007/11880561_28

  • Enhanced byte codes with restricted prefix properties

    Culpepper, J. Shane and Moffat, Alistair (2005). Enhanced byte codes with restricted prefix properties. 12th International Conference on String Processing and Information Retrieval, Buenos Aires Argentina, Nov 02-04, 2005. BERLIN: SPRINGER-VERLAG BERLIN. doi: 10.1007/11575832_1

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Large Language Models for Search and Recommendation

    Large Language Models such as ChatGPT offer enormous promise to users completing everyday tasks. However, these models confidently provide misinformation which can be very convincing. This project aims to explore new techniques to improve the effectiveness of LLMs.

For Media assistance please contact The Office of Marketing and Communications, (07) 3365 1120

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ORCID: 0000-0002-1902-9087

Scopus ID: 13607503000

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