Multi-Order Hypergraph Convolutional Neural Network for Dynamic Social Recommendation System
Yu Wang, Qilong Zhao
Abstract
Recently, online social networks have enriched the users’ lives greatly and social recommendation systems make it easier for users to discover more information that they are interested in. The most advanced graph neural network based social recommendation methods start to utilize the higherorder social relations, e.g. the friends of friends, to reveal users’ preferences. However, existing high-order methods ignore the implicit social relations among users and the users’ interests changing dynamically over time. In this paper, we propose a Multi-Order Hypergraph Convolutional Neural Network (MOHCN) for dynamic social recommendation system to improve the recommendation task, which models the users’ dynamic interest evolution at the session level. To compensate for the lack of social information of some users, we combine the implicit social relations obtained from user-item interaction graph with the explicit social relations from user-user social graph through hypergraph modeling. Extensive experimental results on three real-world datasets demonstrate the effectiveness of our proposed MOHCN compared with the state-of-the-art methods.