论文标题
有监督的对比度学习以推荐
Supervised Contrastive Learning for Recommendation
论文作者
论文摘要
在这项工作中，我们旨在考虑在推荐系统的情况下使用对比度学习的应用，使其更适合推荐任务。我们提出了一个名为“监督对比学习”（SCL）的学习范式，以支持图形卷积神经网络。具体而言，我们将在数据预处理过程中分别计算用户侧和项目方的不同节点之间的相似性，然后在应用对比度学习时，不仅会将增强视图视为正样本，而且将一定数量的相似样本视为正面样本，这与SIMCLR不同，这与其他样本在其他样本中是由batch as batch Assples中的其他样本。我们将SCL应用于最先进的LightGCN。另外，为了考虑节点相互作用的不确定性，我们还提出了一种称为节点复制的新数据增强方法。 Amazon-Book数据集对Gowalla，Yelp2018，Yelp2018的实证研究和消融研究证明了SCL和节点复制的有效性，这提高了建议的准确性和对交互式噪声的鲁棒性。
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.
