Great Explanations: Opinionated Explanations for Recommendations
By Khalil Muhammad, Aonghus Lawlor, Rachael Rafter, Barry Smyth. 2015
43 citations recorded by the source corpus.
Explaining recommendations helps users to make better decisions. We describe a novel approach to explanation for recommender systems, one that drives the recommendation ranking process, while at the same time providing the user with useful insights into the reason why items have been recommended 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.
Subfields: Artificial Intelligence, Information Systems
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Citations in the corpus
- Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “3: Three sampled explanation interfaces designed in Muhammad et al. [2016c] (there are a total of nine interfaces), based on which user study was conducted to evaluate their performance in terms of explainability.”
- A systematic review and taxonomy of explanations in decision support and recommender systems (2017, background): “Four approaches [254,46,157,44] in the e-commerce domain exploited external sources of information, namely product reviews.”