HMKRec: Optimize multi-user representation by hypergraph motifs for knowledge-aware recommendation

Knowledge graph-based recommender systems can explore users’ potential interests by learning user similarities, thereby further improving recommendation performance. However, existing methods focus only on the similarity between two users without considering the interaction patterns among multiple users, which overlook the influence of other users in user representation modeling. In this paper, we propose a novel framework using Hypergraph Motifs to optimize Multi-users representation for Recommendation (HMKRec). Specifically, HMKRec constructs a user–item hypergraph and maps it into a user–user adjacency graph. Then, it utilizes hypergraph motifs to model the interaction patterns of multiple users and reconstructs an implicit relationship network with weights and directions to explore high-order associations among multiple users. To learn the features of items and user relationships, we design a hierarchical graph convolution that integrates hypergraph convolutional networks and graph convolutional networks to obtain high-order representations of users. Finally, we propagate user preferences in the knowledge graph using the attention mechanism to obtain high-order representations of items for recommendation. Extensive experiments on three real-world datasets indicate that our method achieves at least a 1% performance improvement over the best-performing state-of-the-art baselines.