Recommending for People
This page collects resources referenced in my Nov. 21, 2016 talk at the University at Albany, Recommending for People.
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 may be able to meet the needs of different 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 recommendation algorithms meet the needs of different users and among currently-competitive recommendation approaches there is not a clear winner even within the single domain of movie recommendation. I will situate this work within the broader context of our research agenda – including further work on reproducible research, studying the behavior of the user-recommender feedback loop, and tailoring recommenders for particular users – and our vision for designing recommender systems that are responsive to the needs and desires of the people they will affect.
2011. Rethinking The Recommender Research Ecosystem: Reproducibility, Openness, and LensKit. In Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys '11). ACM, 133–140. DOI:10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received)., , , and .
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). ACM, 233–236. DOI:10.1145/2365952.2366002. Acceptance rate: 32%.and .
2016. Behaviorism is Not Enough: Better Recommendations through Listening to Users. In Proceedings of the Tenth ACM Conference on Recommender Systems (RecSys '16). ACM. DOI:10.1145/2959100.2959179. Acceptance rate: 36% (Past, Present, and Future track).and .
2015. Letting Users Choose Recommender Algorithms: An Experimental Study. In Proceedings of the Ninth ACM Conference on Recommender Systems (RecSys '15). ACM. DOI:10.1145/2792838.2800195. Acceptance rate: 21%., , , and .
Work in progress on recommender fairness
Balabanović, Marko, and Yoav Shoham. 1997. “Fab: Content-Based, Collaborative Recommendation.” Commun. ACM 40 (3): 66–72. doi:http://dx.doi.org/10.1145/245108.245124[10.1145/245108.245124].
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:http://dx.doi.org/10.1145/2645710.2645721[10.1145/2645710.2645721].
Resnick, Paul, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. “GroupLens: An Open Architecture for Collaborative Filtering of Netnews.” In ACM CSCW ’94, 175–86. ACM. doi:http://dx.doi.org/10.1145/192844.192905[10.1145/192844.192905].
Sarwar, Badrul, George Karypis, Joseph Konstan, and John Reidl. 2001. “Item-Based Collaborative Filtering Recommendation Algorithms.” In ACM WWW ’01, 285–95. ACM. doi:http://dx.doi.org/10.1145/371920.372071[10.1145/371920.372071].
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.
Burke, Robin. 2002. “Hybrid Recommender Systems: Survey and Experiments.” User Modeling and User-Adapted Interaction 12(4): 331–70. doi:http://dx.doi.org/10.1023/A:1021240730564[10.1023/A:1021240730564].
Rendle, Steffen, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. “BPR: Bayesian Personalized Ranking from Implicit Feedback.” In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, 452–461. UAI ’09. http://dl.acm.org/citation.cfm?id=1795114.1795167.
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:http://dx.doi.org/10.1145/1125451.1125660[10.1145/1125451.1125660].
Franklin, Ursula M. 2004. The Real World of Technology. Revised Edition. Toronto, Ont.; Berkeley, CA: House of Anansi Press.
2016. Dependency Injection with Static Analysis and Context-Aware Policy. Journal of Object Technology 15, 1 (February 2016), pp 1:1–31. DOI:10.5381/jot.2016.15.5.a1.and .