Heterogeneous Graph Contrastive Learning Network for Personalized Micro-Video Recommendation

Personalized micro-video recommendation has attracted a lot of research attention with the growing popularity of micro-video sharing platforms. Many efforts have been made to consider micro-video recommendation as a matching task and shown promising performance, while they only focus on simple features or multi-modal attribute information. Recently, Graph Neural Networks (GNNs) have been employed in many recommendation tasks and achieved impressive success. However, these GNN-based methods may suffer from the following limitations: (1) fail to capture the heterogeneity of nodes in user-video bipartite graphs; (2) ignore the non-local (global) semantic correlation information remained in heterogeneous graphs. In this paper, we present a novel approach, Heterogeneous Graph Contrastive Learning Network (HGCL), for personalized micro-video recommendation. To consider heterogeneity in user-video bipartite graphs, we first introduce a heterogeneous graph encoder network for a high-quality representation learning of users and micro-videos. Specifically, we design a random surfing model to generate node-type specific homogeneous graphs to preserve the heterogeneity. Then we propose a graph contrastive learning framework to achieve representation learning on each node-type specific homogeneous graph by maximizing the mutual information between local patches of a graph and the global representation of the entire graph. Finally, a type-crossing objective function is proposed to jointly integrate the node embeddings from different node types to facilitate high-quality representation learning. Experimental results on real-world datasets in the micro-video recommendation task validate the performance of our method, compared with state-of-the-art baseline algorithms.