Reliable Recommender Systems Research
I have spent considerable effort implementing software and conducting research to help promote reproducibility and reliability in recommender systems research. This has most notably resulted in the LensKit software, an open-source toolkit for recommender systems research. I am still maintaining this software and am active in promoting reproducibility in the RecSys research community.
- 2018–2023: NSF award 1751278, $482,081: CAREER: User-Based Simulation Methods for Quantifying Sources of Error and Bias in Recommender Systems
- Texas State University Research Enhancement Program: Temporal Evaluation of Recommender Systems ($8000, 2015)
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). Cited 1 time.and .
2021. Evaluating Recommenders with Distributions. At Proceedings of the RecSys 2021 Workshop on Perspectives on the Evaluation of Recommender Systems (RecSys '21)., , and .
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. Acceptance rate: 20%. Nominated for Best Long Paper. Cited 78 times., , , , and .
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, 5 pp. Acceptance rate: 47%. Cited 5 times.and .
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. No acceptance rate reported. Cited 24 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. Cited 1 time.and .
2018. The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. SIGIR Forum 52(1) (June 2018), 91–101. Cited 15 times., , , , , , , , , , , , , , , , , , , , and .
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 2018), 96–139. Cited 6 times., , , , , , , , , , , , , , , , , , , , and .
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. No acceptance rate reported. Cited 6 times.and .
2016. Dependency Injection with Static Analysis and Context-Aware Policy. Journal of Object Technology 15(1) (February 2016), 1:1–31.and .
2014. Towards Recommender Engineering: Tools and Experiments in Recommender Differences. Ph.D thesis, University of Minnesota. Cited 5 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. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 173 times., , , and .
2011. RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures. Proceedings of the VLDB Endowment 4(11) (August 2011), 911–920. Acceptance rate: 18%. Cited 12 times., , , , , and .