An Aspect Performance-aware Hypergraph Neural Network for Review-based Recommendation

• URL: http://arxiv.org/abs/2501.15429v1
• Date: Sun, 26 Jan 2025 07:10:22 GMT
• Title: An Aspect Performance-aware Hypergraph Neural Network for Review-based Recommendation
• Authors: Junrui Liu, Tong Li, Di Wu, Zifang Tang, Yuan Fang, Zhen Yang,
• Abstract summary: We propose an aspect performance-aware hypergraph neural network (APH) for the review-based recommendation. APH learns the performance of items from the conflicting sentiment polarity of user reviews. Experiments on six real-world datasets demonstrate that APH improves MSE, Precision@5, and Recall@5 by an average of 2.30%, 4.89%, and 1.60% over the best baseline.
• Score: 13.327249917384144
• License:
• Abstract: Online reviews allow consumers to provide detailed feedback on various aspects of items. Existing methods utilize these aspects to model users' fine-grained preferences for specific item features through graph neural networks. We argue that the performance of items on different aspects is important for making precise recommendations, which has not been taken into account by existing approaches, due to lack of data. In this paper, we propose an aspect performance-aware hypergraph neural network (APH) for the review-based recommendation, which learns the performance of items from the conflicting sentiment polarity of user reviews. Specifically, APH comprehensively models the relationships among users, items, aspects, and sentiment polarity by systematically constructing an aspect hypergraph based on user reviews. In addition, APH aggregates aspects representing users and items by employing an aspect performance-aware hypergraph aggregation method. It aggregates the sentiment polarities from multiple users by jointly considering user preferences and the semantics of their sentiments, determining the weights of sentiment polarities to infer the performance of items on various aspects. Such performances are then used as weights to aggregate neighboring aspects. Experiments on six real-world datasets demonstrate that APH improves MSE, Precision@5, and Recall@5 by an average of 2.30%, 4.89%, and 1.60% over the best baseline. The source code and data are available at https://github.com/dianziliu/APH.

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