Abstract

Recommender systems help people find movies to watch, introduce new friends on social networks, increase sales for online retailers by connecting their customers with personally-relevant products, and direct readers to additional articles on news publishers’ partner sites. Users interact with recommenders almost everywhere they turn on the modern Internet. However, there is a great deal we still do not yet know about how to best design these systems to support their users’ needs and decision-making processes, and how the recommender and its sociotechnical context support and affect each other.

In this talk, I will present work on understanding the ways in which different recommender algorithms impact and respond to their users. This research applies several methodologies, including analysis of recommender algorithms on public data sets and studies of both the stated preferences and observable behaviors of the users of a recommender system. Our findings provide evidence, consistent across different experimental settings, that different users are more satisfied by different recommendation algorithms even within the single domain of movie recommendation. I will also discuss our ongoing work on how recommender algorithms interact with biases in their underlying input data and on deep challenges in evaluating recommender effectiveness with respect to actual user needs.

These projects, along with several others, are a part of our broad vision for designing recommenders and other algorithmic information systems that are responsive to the needs, desires, and well-being of the people they will affect.

Bio

Dr. Michael D. Ekstrand is an assistant professor in the Department of Computer Science at Boise State University, where he studies human-computer interaction and recommender systems. He received his Ph.D in 2014 from the University of Minnesota, supporting reproducible research and examining user-relevant differences in recommender algorithms with the GroupLens research group. He co-leads (with Dr. Sole Pera) the People and Information Research Team (PIReT) at Boise State; is the founder and lead developer of LensKit, an open-source software project aimed at supporting reproducible research and education in recommender systems; and co-created (with Dr. Joseph A. Konstan at the University of Minnesota) the Recommender Systems specialization on Coursera. His research interests are primarily in the ways users and intelligent information systems interact, with the goal of improving the ability of these systems to help their users and produce social benefit, and in the reproducibility of such research.

Slides

• Slides from UCL Mons, June 19
• Slides from JADS, June 20
• RecSys NL at TU Delft, June 26 — slides and video

Resources

• People and Information Research Team
• LensKit software
• MovieLens data sets

Research Presented

• 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), Oct 24, 2011. ACM, pp. 133–140. DOI 10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 259 times. Cited 198 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), May 29, 2017. AAAI, pp. 639–644. No acceptance rate reported. Cited 25 times. Cited 11 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 ’12), Sep 10, 2012. ACM, pp. 233–236. DOI 10.1145/2365952.2366002. Acceptance rate: 32%. Cited 90 times. Cited 73 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 ’14), Oct 6, 2014. ACM, pp. 161–168. DOI 10.1145/2645710.2645737. Acceptance rate: 23%. Cited 301 times. Cited 194 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), Sep 16, 2015. ACM, pp. 11–18. DOI 10.1145/2792838.2800195. Acceptance rate: 21%. Cited 139 times. Cited 104 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), Sep 17, 2016. ACM, pp. 221–224. DOI 10.1145/2959100.2959179. Acceptance rate: 36%. Cited 156 times. Cited 102 times.

• Work in progress on recommender fairness

Works Cited

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• Pera, Maria Soledad, and Yiu-Kai Ng. 2014. “Automating Readers’ Advisory to Make Book Recommendations for K-12 Readers.” In Proceedings of the 8th ACM Conference on Recommender Systems, 9–16. RecSys ’14. New York, NY, USA: ACM. doi:10.1145/2645710.2645721.
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• Sarwar, Badrul M, George Karypis, Joseph A Konstan, and John T Riedl. 2000. “Application of Dimensionality Reduction in Recommender System – A Case Study.” In WebKDD 2000. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.8381.
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• McNee, Sean, John Riedl, and Joseph A. Konstan. 2006. “Making Recommendations Better: An Analytic Model for Human-Recommender Interaction.” In CHI ’06 Extended Abstracts, 1103–8. ACM. doi:10.1145/1125451.1125660.
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• Mehrotra, Rishabh, Ashton Anderson, Fernando Diaz, Amit Sharma, Hanna Wallach, and Emine Yilmaz. 2017. Auditing Search Engines for Differential Satisfaction Across Demographics. In Proceedings of the 26th International Conference on World Wide Web Companion (WWW ’17 Companion).

• 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 17 times.

• Speer, Robyn. 2017. ConceptNet Numberbatch 17.04: better, less-stereotyped word vectors.
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