Great Explanations: Opinionated Explanations for Recommendation
Refereed Conference Meeting Proceeding
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-os 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.
Digital Object Identifer (DOI):
National University of Ireland, Dublin (UCD)
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