Michael D. Ekstrand, Ph.D

Dept. of Computer Science
Boise State University
1910 University Drive
Boise, ID 83725-2055


Ph.D (2014)
Computer Science, University of Minnesota, Minneapolis, MN.
B.S. (2007)
Computer Engineering, Iowa State University, Ames, IA.


Assistant Professor, Dept. of Computer Science, Boise State University
Co-director, People and Information Research Team (PIReT)
Assistant Professor, Dept. of Computer Science, Texas State University


Selected Publications

Author formatting key: myself, advised student, other Boise State student.


Michael D. Ekstrand and Daniel Kluver. 2021. Exploring Author Gender in Book Rating and Recommendation. User Modeling and User-Adapted Interaction (February 2021). DOI 10.1007/s11257-020-09284-2. NSF PAR 10218853. Cited 1 time.


Amifa Raj, Connor Wood, Ananda Montoly, and Michael D. Ekstrand. 2020. Comparing Fair Ranking Metrics. Presented at the 3rd FAccTrec Workshop on Responsible Recommendation (peer-reviewed but not archived). arXiv:2009.01311 [cs.IR]. Cited 6 times.


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. 275–284. DOI 10.1145/3340531.3411962. arXiv:2004.13157 [cs.IR]. NSF PAR 10199451. Acceptance rate: 20%. Nominated for Best Long Paper. Cited 26 times.


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. 2999–3006. DOI 10.1145/3340531.3412778. arXiv:1809.03125 [cs.IR]. NSF PAR 10199450. No acceptance rate reported. Cited 5 times.


Michael D. Ekstrand, Katherine Landau Wright, and Maria Soledad Pera. 2020. Enhancing Classroom Instruction with Online News. Aslib Journal of Information Management 72(5) (June 2020), 725–744. DOI 10.1108/AJIM-11-2019-0309. Cited 2 times.


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. DOI 10.1145/3343413.3378004. arXiv:2001.09455 [cs.IR]. NSF PAR 10146883. Acceptance rate: 47%. Cited 1 time.


Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. 2018. All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT* 2018). PMLR, Proceedings of Machine Learning Research 81:172–186. Acceptance rate: 24%. Cited 64 times.


Michael D. Ekstrand, Rezvan Joshaghani, and Hoda Mehrpouyan. 2018. Privacy for All: Ensuring Fair and Equitable Privacy Protections. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT* 2018). PMLR, Proceedings of Machine Learning Research 81:35–47. Acceptance rate: 24%. Cited 32 times.


Michael D. Ekstrand and Martijn C. Willemsen. 2016. Behaviorism is Not Enough: Better Recommendations through Listening to Users. In Proceedings of the Tenth ACM Conference on Recommender Systems (RecSys ’16, Past, Present, and Future track). ACM. DOI 10.1145/2959100.2959179. Acceptance rate: 36%. Cited 47 times.


Michael D. Ekstrand, Daniel Kluver, F. Maxwell Harper, and Joseph A. Konstan. 2015. Letting Users Choose Recommender Algorithms: An Experimental Study. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15). ACM. DOI 10.1145/2792838.2800195. Acceptance rate: 21%. Cited 74 times.


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 1 time.


Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. User Perception of Differences in Recommender Algorithms. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys ’14). ACM. DOI 10.1145/2645710.2645737. Acceptance rate: 23%. Cited 107 times.


Joseph A. Konstan, J.D. Walker, D. Christopher Brooks, Keith Brown, and Michael D. Ekstrand. 2015. Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. Transactions on Computer-Human Interaction 22(2) (April 2015). DOI 10.1145/2728171. Cited 23 times.


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. 133–140. DOI 10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 161 times.


Michael D. Ekstrand, John T. Riedl, and Joseph A. Konstan. 2011. Collaborative Filtering Recommender Systems. Foundations and Trends® in Human-Computer Interaction 4(2) (February 2011), 81–173. DOI 10.1561/1100000009. Cited 585 times.


Michael Ekstrand, Wei Li, Tovi Grossman, Justin Matejka, and George Fitzmaurice. 2011. Searching for Software Learning Resources Using Application Context. In Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology (UIST ’11). ACM, pp. 195–204. DOI 10.1145/2047196.2047220. Acceptance rate: 25%. Cited 41 times.

Research Funding

Professional Service & Memberships

  • Senior Member, Association for Computing Machinery
  • Co-organizer, TREC 2019–2021 Track on Fairness in Information Retrieval
  • ACM Conference on Recommender Systems (General Co-chair 2018, Steering Committee & SPC member)
  • Conference on Fairness, Accountability, and Transparency (FAccT) (Executive Committee 2020–2022, Steering Committee 2017–present, Network Co-chair, PC 2017–present)
  • Organizer, FATREC Workshop on Responsible Recommendation at RecSys 2017/2018/2020
  • Senior PR member for RecSys & WWW; regular PC for SIGIR, FAccT, UMAP
  • Reviewer for multiple journals, incl. TOIS, TWEB, TKDD, TIIS, TDSC, TKDE, PLOS ONE, and UMUAI