Explaining Recommendations: Satisfaction vs. Promotion 论文

摘要

Recommender systems have become a popular technique for helping users select desirable books, movies, music and other items. Most research in the area has focused on devel-oping and evaluating algorithms for efficiently producing ac-curate recommendations. However, the ability to effectively explain its recommendations to users is another important aspect of a recommender system. The only previous investi-gation of methods for explaining recommendations showed that certain styles of explanations were effective at convinc-ing users to adopt recommendations (i.e. promotion) but failed to show that explanations actually helped users make more accurate decisions (i.e. satisfaction). We present two new methods for explaining recommendations of content-based and/or collaborative systems and experimentally show that they actually improve user’s estimation of item quality.