Multi-aspect Graph Contrastive Learning for Review-enhanced Recommendation

计算机科学 人工智能 机器学习 特征学习 推荐系统 图形 判别式 自编码 深度学习 自然语言处理 理论计算机科学
作者
Ke Wang,Yanmin Zhu,Tianzi Zang,Chunyang Wang,Kuan Liu,Peibo Ma
出处
期刊:ACM Transactions on Information Systems [Association for Computing Machinery]
卷期号:42 (2): 1-29 被引量:2
标识
DOI:10.1145/3618106
摘要

Review-based recommender systems explore semantic aspects of users’ preferences by incorporating user-generated reviews into rating-based models. Recent works have demonstrated the potential of review information to improve the recommendation capacity. However, most existing studies rely on optimizing review-based representation learning part, thus failing to explicitly capture the fine-grained semantic aspects, and also ignoring the intrinsic correlation between ratings and reviews. To address these problems, we propose a multi-aspect graph contrastive learning framework, named MAGCL, with three distinctive designs: (i) a multi-aspect representation learning module, which projects semantic relations to different subspaces by decoupling review information, and then obtains high-order decoupled representations in each aspect via graph encoder. (ii) the contrastive learning module performs graph contrastive learning to capture the correlation between rating and review patterns, which utilize unlabeled data to generate self-supervised signals and, in turn, relieve the data sparsity problem of supervision signals. (iii) the multi-task learning module conducts joint training to learn high-order structure-aware yet self-discriminative node representations by combining recommendation task and self-supervised task, which helps alleviate the over-smoothing problem. Extensive experiments are conducted on four real-world review datasets and the results show the superiority of the proposed framework MAGCL compared with several state of the arts. We also provide further analysis on multi-aspect representations and graph contrastive learning to verify the advantage of proposed framework.
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