Multi-Objective Graph Contrastive Learning for Recommendation

Recently, numerous studies have integrated self-supervised contrastive learning with Graph Convolutional Networks (GCNs) to address the data sparsity and popularity bias to enhance recommendation performance. While such studies have made breakthroughs in accuracy metric, they often neglect non-accuracy objectives such as diversity, novelty and percentage of long-tail items, which greatly reduces the user experience in real-world applications. To this end, we propose a novel graph collaborative filtering model named Multi-Objective Graph Contrastive Learning for recommendation (MOGCL), designed to provide more comprehensive recommendations by considering multiple objectives. Specifically, MOGCL comprises three modules: a multi-objective embedding generation module, an embedding fusion module and a transfer learning module. In the multi-objective embedding generation module, we employ two GCN encoders with different goal orientations to generate node embeddings targeting accuracy and non-accuracy objectives, respectively. These embeddings are then effectively fused with complementary weights in the embedding fusion module. In the transfer learning module, we suggest an auxiliary self-supervised task to promote the maximization of the mutual information of the two sets of embeddings, so that the obtained final embeddings are more stable and comprehensive. The experimental results on three real-world datasets show that MOGCL achieves optimal trade-offs between multiple objectives comparing to the state-of-the-arts.