Supervised Contrastive Learning for Recommendation

• URL: http://arxiv.org/abs/2201.03144v1
• Date: Mon, 10 Jan 2022 03:11:42 GMT
• Title: Supervised Contrastive Learning for Recommendation
• Authors: Chun Yang
• Abstract summary: We propose a supervised contrastive learning framework to pre-train the user-item bipartite graph, and then fine-tune the graph convolutional neural network. We term this learning method as Supervised Contrastive Learning(SCL) and apply it on the most advanced LightGCN.
• Score: 6.407166061614783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compared with the traditional collaborative filtering methods, the graph convolution network can explicitly model the interaction between the nodes of the user-item bipartite graph and effectively use higher-order neighbors, which enables the graph neural network to obtain more effective embeddings for recommendation, such as NGCF And LightGCN. However, its representations is very susceptible to the noise of interaction. In response to this problem, SGL explored the self-supervised learning on the user-item graph to improve the robustness of GCN. Although effective, we found that SGL directly applies SimCLR's comparative learning framework. This framework may not be directly applicable to the scenario of the recommendation system, and does not fully consider the uncertainty of user-item interaction.In this work, we aim to consider the application of contrastive learning in the scenario of the recommendation system adequately, making it more suitable for recommendation task. We propose a supervised contrastive learning framework to pre-train the user-item bipartite graph, and then fine-tune the graph convolutional neural network. Specifically, we will compare the similarity between users and items during data preprocessing, and then when applying contrastive learning, not only will the augmented views be regarded as the positive samples, but also a certain number of similar samples will be regarded as the positive samples, which is different from SimCLR who treats other samples in a batch as negative samples. We term this learning method as Supervised Contrastive Learning(SCL) and apply it on the most advanced LightGCN. In addition, in order to consider the uncertainty of node interaction, we also propose a new data augment method called node replication.

Related papers

• Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation [84.45144851024257]
  CoGCL aims to enhance graph contrastive learning by constructing contrastive views with stronger collaborative information via discrete codes. We introduce a multi-level vector quantizer in an end-to-end manner to quantize user and item representations into discrete codes. For neighborhood structure, we propose virtual neighbor augmentation by treating discrete codes as virtual neighbors. Regarding semantic relevance, we identify similar users/items based on shared discrete codes and interaction targets to generate the semantically relevant view.
  arXiv  Detail & Related papers  (2024-09-09T14:04:17Z)
• Cluster-based Graph Collaborative Filtering [55.929052969825825]
  Graph Convolution Networks (GCNs) have succeeded in learning user and item representations for recommendation systems. Most existing GCN-based methods overlook the multiple interests of users while performing high-order graph convolution. We propose a novel GCN-based recommendation model, termed Cluster-based Graph Collaborative Filtering (ClusterGCF)
  arXiv  Detail & Related papers  (2024-04-16T07:05:16Z)
• Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation [12.945782054710113]
  We propose a novel framework for recommendation tasks called Bilateral Unsymmetrical Graph Contrastive Learning (BusGCL) BusGCL considers the bilateral unsymmetry on user-item node relation density for sliced user and item graph reasoning better with bilateral slicing contrastive training. Comprehensive experiments on two public datasets have proved the superiority of BusGCL in comparison to various recommendation methods.
  arXiv  Detail & Related papers  (2024-03-22T09:58:33Z)
• TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation [1.5836776102398225]
  Long-tailed distribution of entities of KG and noise issues in the real world make item-entity dependent relations deviate from reflecting true characteristics. We design the Two-Level Debiased Contrastive Learning (TDCL) and deploy it in the knowledge graph. Considerable experiments on open-source datasets demonstrate that our method has excellent anti-noise capability.
  arXiv  Detail & Related papers  (2023-10-01T03:56:38Z)
• Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation [13.157762744149966]
  Existing solutions are based on the contrastive graph learning paradigm. We propose a novel Gaussian Graph with Prototypical Contrastive Learning framework to overcome these challenges. We achieve new state-of-the-art performance compared to previous methods on several public datasets.
  arXiv  Detail & Related papers  (2023-07-25T12:56:41Z)
• GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation [58.6450834556133]
  We propose graph contrastive learning to enhance item representations with complex associations from the global view. We extend the CapsNet module with the elaborately introduced target-attention mechanism to derive users' dynamic preferences. Our proposed GUESR could not only achieve significant improvements but also could be regarded as a general enhancement strategy.
  arXiv  Detail & Related papers  (2023-03-01T05:46:36Z)
• LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation [9.181689366185038]
  Graph neural clustering network (GNN) is a powerful learning approach for graph-based recommender systems. In this paper, we propose a simple yet effective graph contrastive learning paradigm LightGCL.
  arXiv  Detail & Related papers  (2023-02-16T10:16:21Z)
• Ordinal Graph Gamma Belief Network for Social Recommender Systems [54.9487910312535]
  We develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions. OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences. We extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model.
  arXiv  Detail & Related papers  (2022-09-12T09:19:22Z)
• Interpolation-based Correlation Reduction Network for Semi-Supervised Graph Learning [49.94816548023729]
  We propose a novel graph contrastive learning method, termed Interpolation-based Correlation Reduction Network (ICRN) In our method, we improve the discriminative capability of the latent feature by enlarging the margin of decision boundaries. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discnative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Self-supervised Graph Learning for Recommendation [69.98671289138694]
  We explore self-supervised learning on user-item graph for recommendation. An auxiliary self-supervised task reinforces node representation learning via self-discrimination. Empirical studies on three benchmark datasets demonstrate the effectiveness of SGL.
  arXiv  Detail & Related papers  (2020-10-21T06:35:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.