Multi-feature fused collaborative attention network for sequential recommendation with semantic-enriched contrastive learning

Recently, graph neural network (GNN) has been widely used in sequential recommendation because of its powerful ability to capture high-order collaborative relations, greatly promoting recommendation performance. However, some existing GNN-based methods fail to make full use of multiple relevant features of nodes and ignore the impact of semantic association between nodes on extracting user preferences. To this end, we propose a multi-feature fused collaborative attention network MASR, which sufficiently learns the temporal and positional features of nodes, and innovatively measures the importance of these two features for analyzing the nodes’ dynamic patterns. In addition, we incorporate semantic-enriched contrastive learning into collaborative filtering to enhance the semantic association between nodes and reduce the noise from the structural neighborhood, which has a positive effect on the sequential recommendation. Compared with the baseline models, the performance of MASR on MovieLens, CDs and Beauty datasets is improved by 2.0%, 2.1% and 1.7% respectively, proving its effectiveness in the sequential recommendation.