User, Agent, Subject, Spy

I gave this talk on November 1, 2018 at the Boise State University AI Club.

My Research

These papers provide more details on the research I presented. Many of them have accompanying code to reproduce the experiments and results.


Michael D. Ekstrand, John T. Riedl, and Joseph A. Konstan. 2011. Collaborative Filtering Recommender Systems. Foundations and Trendsยฎ in Human-Computer Interaction 4(2) (February 2011), 81โ€“173. FnT HCI 4(2) (February 2011). DOI 10.1561/1100000009. Cited 632 times. Cited 1530 times.


Michael Ekstrand and John Riedl. 2012. When Recommenders Fail: Predicting Recommender Failure for Algorithm Selection and Combination. Short paper in Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys โ€™12). ACM, pp.ย 233โ€“236. Proc. RecSys โ€™12. DOI 10.1145/2365952.2366002. Acceptance rate: 32%. Cited 68 times. Cited 76 times.


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 โ€™14). ACM. Proc. RecSys โ€™14. DOI 10.1145/2645710.2645737. Acceptance rate: 23%. Cited 168 times. Cited 234 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 โ€™15). ACM. Proc. RecSys โ€™15. DOI 10.1145/2792838.2800195. Acceptance rate: 21%. Cited 97 times. Cited 108 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 โ€™16, Past, Present, and Future track). ACM. Proc. RecSys โ€™16 (Past, Present, and Future track). DOI 10.1145/2959100.2959179. Acceptance rate: 36%. Cited 81 times. Cited 100 times.


Michael D. Ekstrand, Ion Madrazo Azpiazu, Katherine Landau Wright, and Maria Soledad Pera. 2018. Retrieving and Recommending for the Classroom: Stakeholders, Objectives, Resources, and Users. In Proceedings of the ComplexRec 2018 Second Workshop on Recommendation in Complex Scenarios (ComplexRec โ€™18), at RecSys 2018. Proc. ComplexRec โ€™18. Cited 4 times. Cited 7 times.


Maria Soledad Pera, Katherine Wright, and Michael D. Ekstrand. 2018. Recommending Texts to Children with an Expert in the Loop. In Proceedings of the 2nd International Workshop on Children & Recommender Systems (KidRec โ€™18), at IDC 2018. Proc. KidRec โ€™18. DOI 10.18122/cs_facpubs/140/boisestate. Cited 7 times. Cited 6 times.


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.ย 639โ€“644. (Recommender Systems track). No acceptance rate reported. Cited 9 times. Cited 13 times.


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.


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 81:172โ€“186. Proc. FAT* 2018. Acceptance rate: 24%. Cited 170 times. Cited 190 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 โ€™20, Resource track). ACM, pp.ย 2999โ€“3006. Proc. CIKM โ€™20 (Resource track). DOI 10.1145/3340531.3412778. arXiv:1809.03125. NSF PAR 10199450. No acceptance rate reported. Cited 45 times. Cited 63* 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 โ€™11). ACM, pp.ย 133โ€“140. Proc. RecSys โ€™11. DOI 10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 191 times. Cited 226 times.


Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, and Daniel Kluver. 2018. Exploring Author Gender in Book Rating and Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys โ€™18). ACM, pp.ย 242โ€“250. Proc. RecSys โ€™18. DOI 10.1145/3240323.3240373. arXiv:1808.07586v1. Acceptance rate: 17.5%. Citations reported under UMUAI21. Citations reported under UMUAI21.


Other Work Cited