Hierarchical Graph Contrastive Learning for Review-Enhanced Recommendation
Pages 423 - 440
Abstract
In comparison to numerical ratings and implicit feedback, textual reviews offer a deeper understanding of user preferences and item attributes. Recent research underscores the potential of reviews in augmenting recommendation capabilities, thereby advancing the deployment of review-enhanced recommendation systems. However, existing methodologies often neglect the significance of rating magnitudes and are susceptible to challenges such as data sparsity and long-tail distribution in real-world contexts. To address these challenges, we propose Hierarchical Graph Contrastive Learning (HGCL) for advancing review-enhanced recommendation systems. HGCL dynamically learns hypergraph structures to capture higher-order correlations among nodes and simultaneously integrates local and global collaborative relations through global-local contrastive learning. Additionally, we propose hierarchical graph contrastive learning methods to better model the intrinsic correlation between ratings and reviews, encompassing aspects such as local-global, cross-rating, and edge-level contrastive learning. Extensive experimentation on five public datasets demonstrates that the proposed method notably outperforms state-of-the-art approaches.
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Published In
Sep 2024
508 pages
ISBN:978-3-031-70364-5
DOI:10.1007/978-3-031-70365-2
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Springer-Verlag
Berlin, Heidelberg
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Published: 08 September 2024
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