Natural language justifications for recommender systems exploiting text summarization and sentiment analysis

This paper reports and summarizes the methodology presented in [16] and accepted for publication at ACM RecSys 20191. In this work we present a methodology to justify recommendations that relies on the information extracted from users’ reviews discussing the available items. The intuition behind the approach is to conceive the justification as a summary of the most relevant and distinguishing aspects of the item, automatically obtained by analyzing its reviews. To this end, we designed a pipeline of natural language processing techniques including aspect extraction, sentiment analysis and text summarization to gather the reviews, process the relevant excerpts, and generate a unique synthesis presenting the main characteristics of the item. Such a summary is finally presented to the target user as a justification of the received recommendation. In the experimental evaluation we carried out a user study in the movie domain (N=141) and the results showed that our approach is able to make the recommendation process more transparent, engaging and trustful for the users. Moreover, the proposed method also beat another review-based explanation technique, thus confirming the validity of our intuition.