Document Type
Conference Proceeding
Publication Date
2016
DOI
https://doi.org/10.1145/2959100.2959179
Abstract
Behaviorism is the currently-dominant paradigm for building and evaluating recommender systems. Both the operation and the evaluation of recommender system applications are most often driven by analyzing the behavior of users. In this paper, we argue that listening to what users say — about the items and recommendations they like, the control they wish to exert on the output, and the ways in which they perceive the system — and not just observing what they do will enable important developments in the future of recommender systems. We provide both philosophical and pragmatic motivations for this idea, describe the various points in the recommendation and evaluation processes where explicit user input may be considered, and discuss benefits that may result from considered incorporation of user preferences at each of these points. In particular, we envision recommender applications that aim to support users’ better selves: helping them live the life that they desire to lead. For example, recommender-assisted behavior change requires algorithms to predict not what users choose or do now, inferable from behavioral data, but what they should choose or do in the future to become healthier, fitter, more sustainable, or culturally aware. We hope that our work will spur useful discussion and many new ideas for recommenders that empower their users.
Copyright Statement
This is an author-produced, peer-reviewed version of this article. The final, definitive version of this document can be found online at RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems, published by Association for Computing Machinery. Copyright restrictions may apply. doi: 10.1145/2959100.2959179
Presentation slides associated with the content of this article
Publication Information
Ekstrand, Michael D. and Willemsen, Martijn C.. (2016). "Behaviorism is Not Enough: Better Recommendations Through Listening to Users". RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems, 221-224.