A Recommendation Algorithm Based on Automatic Meta-path Generation and Relationship Aggregation

Abstract

Knowledge Graph (KG) contains rich semantic information and supports knowledge reasoning. In recent years, introducing KG as auxiliary information into the recommender system has become one common measure for improving recommendation quality. The unified graph, which is constructed from the KG and user-item matrix in recommender systems, contains meta-paths formed by single-hop/continuous multi-hop connectivity relationships, and these meta-paths can assist modeling of user preferences. The quality of manually designed meta-paths is prone to the type and number of human-defined meta-paths. Moreover, the process of defining meta-paths is time-consuming and labor-intensive, and inadequate sufficient considerations in design will have an adverse impact on the quality of recommendations. We propose a self-supervised meta-path generation approach that does not rely on domain knowledge to select valuable path information from the unified graph and can deliver high-quality recommendations and reduce noises. Previous studies on meta-paths mainly focused on the neighbor information of nodes and ignored the edges that represents relationships between nodes. We develop a meta-path-based relational path-aware strategy to discover the relational information included within the meta-path. To make the use of the global structure in the unified graph and the information within the local scope in the user-item bipartite graph and KG, a two-level relationship aggregator to fully aggregate the fine-grained semantic information and multi-hop semantic associations is also proposed. We conducted experiments on two public datasets, MovieLens and Book-Crossing to verify the effectiveness of the proposed algorithm. The experimental results show that the recommendation algorithm outperforms the baseline models in terms of AUC, Recall@K, and F1 in most cases.