Explaining Recommendations Based on Feature Sentiments in Product Reviews

The explanation interface has been recognized important in recommender systems as it can help users evaluate recommendations in a more informed way for deciding which ones are relevant to their interests. In different decision environments, the specific aim of explanation can be different. In high-investment product domains (e.g., digital cameras, laptops) for which users usually attempt to avoid financial risk, how to support users to construct stable preferences and make better decisions is particularly crucial. In this paper, we propose a novel explanation interface that emphasizes explaining the tradeoff properties within a set of recommendations in terms of both their static specifications and feature sentiments extracted from product reviews. The objective is to assist users in more effectively exploring and understanding product space, and being able to better formulate their preferences for products by learning from other customers' experiences. Through two user studies (in form of both before-after and within-subjects experiments), we empirically identify the practical role of feature sentiments in combination with static specifications in producing tradeoff-oriented explanations. Specifically, we find that our explanation interface can be more effective to increase users' product knowledge, preference certainty, perceived information usefulness, recommendation transparency and quality, and purchase intention.