Great Explanations: Opinionated Explanations for Recommendation
|Title:||Great Explanations: Opinionated Explanations for Recommendation||Authors:||Muhammad, Khalil
|Permanent link:||http://hdl.handle.net/10197/8110||Date:||30-Sep-2015||Abstract:||Explaining recommendations helps users to make better, more satisfying decisions. We describe a novel approach to explanation for recommender 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 ofa case-based recommender system that harnesses opinions mined from user-generated reviews, and evaluate it on TripAdvisor Hotel data.||Type of material:||Conference Publication||Publisher:||Springer||Copyright (published version):||2015 Springer||Keywords:||Machine learning;Statistics;Recommender systems;Case-based reasoning;Explanations;Opinion mining;Sentiment analysis||DOI:||10.1007/978-3-319-24586-7_17||Language:||en||Status of Item:||Peer reviewed||Is part of:||Hullermeier, E. and Minor, M. (eds.).Proceedings of Case-based Reasoning Research and Development: 23rd International Conference, ICCBR 2015, Frankfurt am Main, Germany 28-30 September 2015||Conference Details:||Case-based Reasoning Research and Development: 23rd International Conference, ICCBR 2015, Frankfurt am Main, Germany 28-30 September 2015|
|Appears in Collections:||Computer Science Research Collection|
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