Michael Ekstrand

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.

Funding

POPROX

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

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.

Papers
CIKM20-lk
2020

Michael D. Ekstrand. 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.Β 2999–3006. Proc. CIKM ’20 (Resource track). DOI 10.1145/3340531.3412778. arXiv:1809.03125. NSF PAR 10199450. No acceptance rate reported. Cited 44 times. Cited 63* times.

JOT16
2016

Michael D. Ekstrand and Michael Ludwig. 2016. Dependency Injection with Static Analysis and Context-Aware Policy. Journal of Object Technology 15(1) (February 2016), 1:1–31. DOI 10.5381/jot.2016.15.1.a1. Cited 14 times.

⸘2014β€½
2014

Michael D. Ekstrand. 2014. Towards Recommender Engineering: Tools and Experiments in Recommender Differences.

Ph.D thesis, University of Minnesota.

HDL 11299/165307. Cited 4 times.Cited 8 times.

CSW14
2014

Michael D. Ekstrand. 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.

RecSys11
2011

Michael D. Ekstrand, Michael Ludwig, Joseph A. Konstan, and John T. Riedl. 2011. Rethinking The Recommender Research Ecosystem: Reproducibility, Openness, and LensKit. In Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys ’11). ACM, pp.Β 133–140. Proc. RecSys ’11. DOI 10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 190 times. Cited 226 times.

RSDemos11
2011

Michael D. Ekstrand, Michael Ludwig, Jack Kolb, and John T. Riedl. 2011. LensKit: A Modular Recommender Framework. Demo recorded in Proceedings of the 5th ACM Conference on Recommender Systems (RecSys ’11). ACM, pp.Β 349-350. Proc. RecSys ’11. DOI 10.1145/2043932.2044001. Cited 1 time. Cited 43 times.

Evaluation Practice

I have also done a variety of work on evaluating recommender systems, in addition to our work on fairness.

Papers
TORS23
2023

Michael D. Ekstrand, Ben Carterette, and Fernando Diaz. 2023. Distributionally-Informed Recommender System Evaluation. Transactions on Recommender Systems (August 2023). TORS (August 2023). DOI 10.1145/3613455. arXiv:2309.05892. NSF PAR 10461937.

RSPE21-inf
2021

Ngozi Ihemelandu and Michael D. Ekstrand. 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. DOI 10.48550/arXiv.2109.06424. arXiv:2109.06424. Cited 5 times. Cited 3 times.

RSPE21-dist
2021

Michael D. Ekstrand, Ben Carterette, and Fernando Diaz. 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.

CIKM20-ee
2020

Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, and Ben Carterette. 2020. Evaluating Stochastic Rankings with Expected Exposure. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20). ACM, pp.Β 275–284. Proc. CIKM ’20. DOI 10.1145/3340531.3411962. arXiv:2004.13157. NSF PAR 10199451. Acceptance rate: 20%. Nominated for Best Long Paper. Cited 119 times. Cited 115 times.

CHIIR20
2020

Mucun Tian and Michael D. Ekstrand. 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. Proc. CHIIR ’20. DOI 10.1145/3343413.3378004. arXiv:2001.09455. NSF PAR 10146883. Acceptance rate: 47%. Cited 7 times. Cited 7 times.

Reveal18-mc
2018

Mucun Tian and Michael D. Ekstrand. 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.

FORUM18
2018

Nicola Ferro, Norbert Fuhr, Gregory Grefenstette, Joseph A. Konstan, Pablo Castells, Elizabeth M. Daly, Thierry Declerck, Michael D. Ekstrand, Werner Geyer, Julio Gonzalo, Tsvi Kuflik, Krister LindΓ©n, Bernardo Magnini, Jian-Yun Nie, Raffaele Perego, Bracha Shapira, Ian Soboroff, Nava Tintarev, Karin Verspoor, Martijn C. Willemsen, and Justin Zobel. 2018. The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. SIGIR Forum 52(1) (June 2018), 91–101. DOI 10.1145/3274784.3274789. Cited 18 times. Cited 15 times.

DAGMAN18
2018

Nicola Ferro, Norbert Fuhr, Gregory Grefenstette, Joseph A. Konstan, Pablo Castells, Elizabeth M. Daly, Thierry Declerck, Michael D. Ekstrand, Werner Geyer, Julio Gonzalo, Tsvi Kuflik, Krister LindΓ©n, Bernardo Magnini, Jian-Yun Nie, Raffaele Perego, Bracha Shapira, Ian Soboroff, Nava Tintarev, Karin Verspoor, Martijn C. Willemsen, and Justin Zobel. 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. DOI 10.4230/DagMan.7.1.96. Cited 15 times. Cited 18 times.

FLAIRS17-s
2017

Michael D. Ekstrand and Vaibhav Mahant. 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.Β 639–644. (Recommender Systems track). No acceptance rate reported. Cited 8 times. Cited 13 times.

VLDB11
2011

Justin J. Levandoski, Michael D. Ekstrand, Michael J. Ludwig, Ahmad Eldawy, Mohamed F. Mokbel, and John T. Riedl. 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 9 times. Cited 21 times.