Learning high-order user-item relation via hyperedge for recommender system

Recommender systems aim to find candidate items that are likely to be interesting to users based on their potential preferences. Existing methods mainly leverage user-item interaction data to learn a pairwise relation between a user and an item, or incorporate the social relations of users from a social network to model high-order relations among multiple users. However, complex relations exist not only among users but also among items, and high-order relations among users and items are vital for recommendation. For example, people buy products because of different latent reasons, which can be captured by relations involving multiple items. Such a high-order user-item relations have barely been studied in existing research. In this paper, we seek to extract high-order relations involving both users and items from interaction data and construct hyperedges to represent these relations. Specifically, we identify latent factors between users and items as the hyperedges, which is performed by matrix factorization on the interaction matrix. After that, we develop a hypergraph convolutional network based on hypergraph expansion to learn embeddings for users, items, as well as high-order relations in a joint representation space. By doing this, high-order relations involving multiple users and items are exploited to learn comprehensive representations of users and items for recommendation. The experimental results on several real-world datasets demonstrate the effectiveness of our proposed method.