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.
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