Beyond Algorithms: An HCI Perspective on Recommender Systems

The accuracy of recommendations made by an online Recommender System (RS) is mostly dependent on the underlying collaborative filtering algorithm. However, the ultimate effectiveness of an RS is dependent on factors that go beyond the quality of the algorithm. The goal of an RS is to introduce users to items that might interest them, and convince users to sample those items. What design elements of an RS enable the system to achieve this goal? To answer this question, we examined the quality of recommendations and usability of three book RS (Amazon.com, RatingZone & Sleeper) and three movie RS (Amazon.com, MovieCritic, Reel.com). Our findings indicate that from a user’s perspective, an effective recommender system inspires trust in the system; has system logic that is at least somewhat transparent; points users towards new, not-yet-experienced items; provides details about recommended items, including pictures and community ratings; and finally, provides ways to refine recommendations by including or excluding particular genres. Users expressed willingness to provide more input to the system in return for more effective recommendations.