Social domain integrated semantic self-discovery method for recommendation

Recommender systems effectively improve the convenience for users to access interesting information from vast amounts of data resources. However, the issue of data sparsity significantly impacts recommendation performance. Heterogeneous information network (HIN), as a form of heterogeneous graph data representation, remarkably enhances recommendation performance. Despite this, current HIN-based recommendations insufficiently consider the social attributes and multidimensional interaction information of users. Additionally, the problem of expert experience dependence on extracting semantic features leads to limited generalization performance. To address these issues, we propose the social domain integrated semantic self-discovery method for recommendation(SSRec). This method constructs a social domain information fusion network to fuse multidimensional interaction information of users in the social domain and designs a multidimensional semantic knowledge mining method to extract semantic knowledge of different meta-paths. We also propose a semantic relationship self-discovery method of heterogeneous nodes that utilizes deep reinforcement learning to address the limited efficiency of traditional manual meta-paths selection. Simulation results show that the SSRec can effectively improve the recommendation performance compared with baseline methods, achieving high-precision recommendations.