Hypergraphs with attention on reviews for explainable recommendation

• Main Authors: JENDAL, Theis E., LE, Trung Hoang, LAUW, Hady Wirawan, LISSANDRINI, Matteo, DOLOG, Peter, HOSE, Katja
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/8724; https://ink.library.smu.edu.sg/context/sis_research/article/9727/viewcontent/ecir24.pdf
• Institution: Singapore Management University

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.