Supervised Contrastive Learning for Recommendation
This work addresses the challenge of making contrastive learning more effective for recommendation tasks, offering incremental improvements for users and developers in recommendation systems.
The authors tackled the problem of applying contrastive learning to recommendation systems by proposing a supervised contrastive learning (SCL) paradigm and a node replication data augmentation method, which improved recommendation accuracy and robustness to noise on datasets like Gowalla, Yelp2018, and Amazon-Book.
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 learning paradigm called supervised contrastive learning(SCL) to support the graph convolutional neural network. Specifically, we will calculate the similarity between different nodes in user side and item side respectively 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 with SimCLR that treats other samples in a batch as negative samples. We apply SCL 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. Empirical research and ablation study on Gowalla, Yelp2018, Amazon-Book datasets prove the effectiveness of SCL and node replication, which improve the accuracy of recommendations and robustness to interactive noise.