Opinionated explanations for recommendation systems
Refereed Conference Meeting Proceeding
This paper describes a novel approach for generating explanations for recommender systems based on opinions in user-generated reviews. We show how these opinions can be used to construct helpful and compelling explanations at recommendation time. The explanation highlights how the pros and cons of a recommended item compares to alternative items. We propose a way to score these explanations based on their content. The scores help to identify compelling explanations, providing a strong reason why the item being explained is better or worse than the alternatives. We describe the results of offline experiments and a live-user study based on TripAdvisor data to demonstrate the usefulness of this approach.
International Conference on Innovative Techniques and Applications of Artificial Intelligence
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National University of Ireland, Dublin (UCD)
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