Behaviorism is not enough: Better recommendations through listening to users

Abstract

Behaviorism is the currently-dominant paradigm for building and evaluating recommender systems. Both the operation and the eval-uation of recommender system applications are most often driven by analyzing the behavior of users. In this paper, we argue that lis-tening 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 recom-mender systems. We provide both philosophical and pragmatic motivations for this idea, describe the various points in the recom-mendation 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 partic-ular, 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 fu-ture 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.