Opinionated Explanations for Recommendation Systems

Abstract

Explaining recommendations helps users to make better, more satisfying decisions. We describe a novel approach to explanation for rec-ommender systems, one that drives the recommendation process, while at the same time providing the user with useful insights into the reason why items have been chosen and the trade-offs they may need to consider when making their choice. We describe this approach in the context of a case-based recommender system that harnesses opinions mined from user-generated reviews, and evaluate it on TripAdvisor Hotel data.