Research Methods and Infrastructure
I have a number of previous and ongoing projects to improve recommender systems research methods and infrastructure to support research. This has most notably resulted in the LensKit software, an open-source toolkit for recommender systems research, and more recently the new POPROX project to build online infrastructure for user-facing recommender systems research. POPROX is a new project to develop a news recommendation platform that will serve as shared infrastructure to support academic research on recommender systems with actual user responses. It’s just kicking off, and should be ready for experiments in 2024. I am actively recruiting a Ph.D student for this project. LensKit is an open-source toolkit supporting recommender systems research and education. Originally released for Java in 2010, I rewrote it in Python in 2018. It has been used to support dozens of published papers. 2020. LensKit for Python: Next-Generation Software for Recommender Systems Experiments. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20, Resource track). ACM, pp. 2999–3006. Proc. CIKM ’20 (Resource track). DOI 10.1145/3340531.3412778. arXiv:1809.03125 [cs.IR]. NSF PAR 10199450. No acceptance rate reported. Cited 72 times. Cited 50 times. 2016. Dependency Injection with Static Analysis and Context-Aware Policy. Journal of Object Technology 15(1) (February 1st, 2016), 1:1–31. DOI 10.5381/jot.2016.15.1.a1. Cited 14 times. 2014. Towards Recommender Engineering: Tools and Experiments in Recommender Differences. Ph.D thesis, University of Minnesota. HDL 11299/165307. Cited 8 times. Cited 4 times. 2014. Building Open-Source Tools for Reproducible Research and Education. At Sharing, Re-use, and Circulation of Resources in Cooperative Scientific Work, a workshop at CSCW 2014. 2011. Rethinking The Recommender Research Ecosystem: Reproducibility, Openness, and LensKit. In Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys ’11). ACM, pp. 133–140. Proc. RecSys ’11. DOI 10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 233 times. Cited 195 times. 2011. LensKit: A Modular Recommender Framework. Demo recorded in Proceedings of the 5th ACM Conference on Recommender Systems (RecSys ’11). ACM, pp. 349-350. Proc. RecSys ’11. DOI 10.1145/2043932.2044001. Cited 43 times. Cited 2 times. I have also done a variety of work on evaluating recommender systems, in addition to our work on fairness. 2023. Candidate Set Sampling for Evaluating Top-N Recommendation. In Proceedings of the 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’23). Proc. WI-IAT ’23. DOI 10.1109/WI-IAT59888.2023.00018. arXiv:2309.11723 [cs.IR]. Acceptance rate: 28%. 2023. Distributionally-Informed Recommender System Evaluation. Transactions on Recommender Systems (August 5th, 2023). TORS (August 5th, 2023). DOI 10.1145/3613455. arXiv:2309.05892 [cs.IR]. NSF PAR 10461937. 2021. Statistical Inference: The Missing Piece of RecSys Experiment Reliability Discourse. In Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2021 (RecSys ’21). Proc. PERSPECTIVES @ RecSys ’21. arXiv:2109.06424 [cs.IR]. Cited 5 times. Cited 6 times. 2021. Evaluating Recommenders with Distributions. At Proceedings of the RecSys 2021 Workshop on Perspectives on the Evaluation of Recommender Systems (RecSys ’21). Proc. PERSPECTIVES @ RecSys ’21. Cited 2 times. 2020. Evaluating Stochastic Rankings with Expected Exposure. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20). ACM, pp. 275–284. Proc. CIKM ’20. DOI 10.1145/3340531.3411962. arXiv:2004.13157 [cs.IR]. NSF PAR 10199451. Acceptance rate: 20%. Nominated for Best Long Paper. Cited 137 times. Cited 127 times. 2020. Estimating Error and Bias in Offline Evaluation Results. Short paper in Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (CHIIR ’20). ACM, pp. 5. Proc. CHIIR ’20. DOI 10.1145/3343413.3378004. arXiv:2001.09455 [cs.IR]. NSF PAR 10146883. Acceptance rate: 47%. Cited 10 times. Cited 8 times. 2018. Monte Carlo Estimates of Evaluation Metric Error and Bias. Computer Science Faculty Publications and Presentations 148. Boise State University. Presented at the REVEAL 2018 Workshop on Offline Evaluation for Recommender Systems, a workshop at RecSys 2018. DOI 10.18122/cs_facpubs/148/boisestate. NSF PAR 10074452. Cited 1 time. Cited 1 time. 2018. The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. SIGIR Forum 52(1) (June 1st, 2018), 91–101. DOI 10.1145/3274784.3274789. Cited 15 times. Cited 17 times. 2018. From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442). Dagstuhl Manifestos 7(1) (November 21st, 2018), 96–139. DOI 10.4230/DagMan.7.1.96. Cited 20 times. Cited 15 times. 2017. Sturgeon and the Cool Kids: Problems with Random Decoys for Top-N Recommender Evaluation. In Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (Recommender Systems track). AAAI, pp. 639–644. (Recommender Systems track). No acceptance rate reported. Cited 15 times. Cited 9 times. 2011. RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures. Proceedings of the VLDB Endowment 4(11) (August 1st, 2011), 911–920. Acceptance rate: 18%. Cited 21 times. Cited 9 times.Funding
POPROX
LensKit
Papers
Evaluation Practice
Papers