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.
CITATION STYLE
Lawlor, A., Muhammad, K., Rafter, R., & Smyth, B. (2015). Opinionated Explanations for Recommendation Systems. In Research and Development in Intelligent Systems XXXII (pp. 331–344). Springer International Publishing. https://doi.org/10.1007/978-3-319-25032-8_25
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