Affiliation:
1. Shanghai Jiao Tong University, China
Abstract
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.
Funder
National Science Foundation of China
Shanghai Municipal Science and Technology Commission
Shanghai East Talents Program and Zhejiang Aoxin Co. Ltd
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems