  The top-k recommendation is a fundamental task in recommendation systems
which is generally learned by comparing positive and negative pairs. The
Contrastive Loss (CL) is the key in contrastive learning that has received more
attention recently and we find it is well suited for top-k recommendations.
However, it is a problem that CL treats the importance of the positive and
negative samples as the same. On the one hand, CL faces the imbalance problem
of one positive sample and many negative samples. On the other hand, positive
items are so few in sparser datasets that their importance should be
emphasized. Moreover, the other important issue is that the sparse positive
items are still not sufficiently utilized in recommendations. So we propose a
new data augmentation method by using multiple positive items (or samples)
simultaneously with the CL loss function. Therefore, we propose a Multi-Sample
based Contrastive Loss (MSCL) function which solves the two problems by
balancing the importance of positive and negative samples and data
augmentation. And based on the graph convolution network (GCN) method,
experimental results demonstrate the state-of-the-art performance of MSCL. The
proposed MSCL is simple and can be applied in many methods. We will release our
code on GitHub upon the acceptance.
