Explanations that are Intrinsic to Recommendations
Arpit Rana, Derek Bridge · 2018
Explanations can give credibility to recommendations and help users to make better choices. In current recommender systems, explanation is a step that comes after recommendation. In this paper, we describe an approach that turns recommender systems on their head. In our approach, which we call Recommendation-by-Explanation (hr-by-e ), the system constructs a reason, or explanation, for recommending each candidate item; then it recommends those candidate items that have the best explanations. By unifying recommendation and explanation, r-by-e finds relevant recommendations with explanations that have a high degree of fidelity. We present the results of an offline experiment using a movie recommendation dataset. We show that r-by-e achieves higher precision than a comparable recommender, while both produce recommendations with roughly equal levels of diversity and serendipity. We also present the results of deploying a web-based system through which we have conducted two user trials. In one trial, we evaluate recommendation quality. Participants in this trial found r-by-e's recommendations to be more diverse, serendipitous and relevant than those of the competitor system. In another trial, we evaluate explanation quality. We used a re-rating task: users rated recommendations initially in the case where they were given only the explanation and not the identity of the movie, and then re-rated in the case where they were given information about the recommended movie. We found a stronger correlation between the pairs of ratings in the case of r-by-e. This suggests that r-by-e's explanations allow users to make more accurate judgments about the quality of recommended items.