Hypergraphs with Attention on Reviews for Explainable Recommendation
Given a recommender system based on reviews, the chal-lenges are how to effectively represent the review data and how to explainthe produced recommendations. We propose a novel review-specific Hy-pergraph (HG) model, and further introduce a model-agnostic explaina-bility module. The HG model captures high-order connections betweenusers, items, aspects, and opinions while maintaining information aboutthe review. The explainability module can use the HG model to ex-plain a prediction generated by any model. We propose a path-restrictedreview-selection method biased by the user preference for item reviewsand propose a novel explanation method based on a review graph. Ex-periments on real-world datasets confirm the ability of the HG model tocapture appropriate explanations.
Code (2)
Tasks
Explainable RecommendationRecommendation SystemsSimilar Papers 제목 키워드 기반
PERS: A Personalized and Explainable POI Recommender System
The Location-Based Social Networks (LBSN) (e.g., Facebook) have many factors (for instance, ratings, check-in time, etc.) that play a crucial role for the Point-of-Interest (POI) recommendations. Unlike ratings, the revi…
Explainable RecommendationRecommendation SystemsExplainable Recommender with Geometric Information Bottleneck
Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated rationales to train models for explanat…
Explanation GenerationRecommendation SystemsAOTree: Aspect Order Tree-based Model for Explainable Recommendation
Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recommendations only consider the importance of…
Decision MakingExplainable RecommendationRecommendation SystemsSynthesizing Aspect-Driven Recommendation Explanations from Reviews
Explanations help to make sense of recommendations, increasing the likelihood of adoption. However, existing approaches to explainable recommendations tend to rely on rigid, standardized templates, customized only via fi…
Explainable RecommendationText GenerationVisually Explainable Recommendation
Images account for a significant part of user decisions in many application scenarios, such as product images in e-commerce, or user image posts in social networks. It is intuitive that user preferences on the visual pat…
Explainable RecommendationRecommendation Systems