Multi-aspect Graph Contrastive Learning for Review-enhanced Recommendation
Article No.: 51, Pages 1 - 29
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.
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Index Terms
- Multi-aspect Graph Contrastive Learning for Review-enhanced Recommendation
Recommendations
A Review-aware Graph Contrastive Learning Framework for Recommendation
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalMost modern recommender systems predict users' preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the ...
SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation
WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data MiningContrastive-learning-based neural networks have recently been introduced to recommender systems, due to their unique advantage of injecting collaborative signals to model deep representations, and the self-supervision nature in the learning process. ...
Cross-View Sample-Enriched Graph Contrastive Learning Network for Personalized Micro-video Recommendation
ICMR '23: Proceedings of the 2023 ACM International Conference on Multimedia RetrievalMicro-video recommendation has attracted extensive research attention with the increasing popularity of micro-video sharing platforms. Recently, graph contrastive learning (GCL) is adopted for enhancing the performance of graph neural network based ...
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Publication History
Published: 08 November 2023
Online AM: 05 September 2023
Accepted: 22 August 2023
Revised: 21 August 2023
Received: 06 December 2022
Published in TOIS Volume 42, Issue 2
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