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Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation

arXiv 2403.15075 ↗First posted 22 Mar 2024cs.IR cs.AIcited by 0

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Abstract (arXiv)

Recent methods utilize graph contrastive Learning within graph-structured user-item interaction data for collaborative filtering and have demonstrated their efficacy in recommendation tasks. However, they ignore that the difference relation density of nodes between the user- and item-side causes the adaptability of graphs on bilateral nodes to be different after multi-hop graph interaction calculation, which limits existing models to achieve ideal results. To solve this issue, we propose a novel framework for recommendation tasks called Bilateral Unsymmetrical Graph Contrastive Learning (BusGCL) that consider the bilateral unsymmetry on user-item node relation density for sliced user and item graph reasoning better with bilateral slicing contrastive training. Especially, taking into account the aggregation ability of hypergraph-based graph convolutional network (GCN) in digging implicit similarities is more suitable for user nodes, embeddings generated from three different modules: hypergraph-based GCN, GCN and perturbed GCN, are sliced into two subviews by the user- and item-side respectively, and selectively combined into subview pairs bilaterally based on the characteristics of inter-node relation structure. Furthermore, to align the distribution of user and item embeddings after aggregation, a dispersing loss is leveraged to adjust the mutual distance between all embeddings for maintaining learning ability. Comprehensive experiments on two public datasets have proved the superiority of BusGCL in comparison to various recommendation methods. Other models can simply utilize our bilateral slicing contrastive learning to enhance recommending performance without incurring extra expenses.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 28 references that are in the map. First posted on arXiv 22 Mar 2024.

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References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  3. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  4. 2019Neural Graph Collaborative Filteringcited by 661
  5. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  6. 2020Self-supervised Graph Learning for Recommendationcited by 305
  7. 2021Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendationcited by 173
  8. 2020A Survey on Knowledge Graph-Based Recommender Systemscited by 170
  9. 2017Fast Matrix Factorization for Online Recommendation with Implicit Feedbackcited by 101
  10. 2021Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendationcited by 101
  11. 2022Self-Supervised Learning for Recommender Systems: A Surveycited by 95
  12. 2022Hypergraph Contrastive Collaborative Filteringcited by 63
5 more
  1. 2020Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systemscited by 38
  2. 2022Self-Supervised Hypergraph Transformer for Recommender Systemscited by 32
  3. 2023SSLRec: A Self-Supervised Learning Framework for Recommendationcited by 17
  4. 2020Self-supervised Learning for Large-scale Item Recommendationscited by 11
  5. 2019Generating Reliable Friends via Adversarial Training to Improve Social Recommendationcited by 8

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