Hypergraphs with Attention on Reviews for Explainable Recommendation
Published in ECIR: European Conference on Information Retrieval, 2024
Given a recommender system based on reviews, the challenges are how to effectively represent the review data and how to explain the produced recommendations. We propose a novel review-specific Hypergraph (HG) model, and further introduce a model-agnostic explainability module. The HG model captures high-order connections between users, items, aspects, and opinions while maintaining information about the review. The explainability module can use the HG model to explain a prediction generated by any model. We propose a path-restricted review-selection method biased by the user preference for item reviews and propose a novel explanation method based on a review graph. Experiments on real-world datasets confirm the ability of the HG model to capture appropriate explanations.
Recommended citation: T.E. Jendal, TH. Le, H.W. Lauw, M. Lissandrini, P. Dolog, K. Hose, "Hypergraphs with Attention on Reviews for Explainable Recommendation," in ECIR, 2024
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