UCBiG-Plugin:改进图神经网络协同过滤的通用插入式框架OA

Graph neural networks have become a new technology for collaborative filtering.Although they can iteratively aggregate neighbor-hood information and naturally capture higher-order collaborative signals,most of the related work is carried out on the user item bipartite graph.However,the alternating connection between users and items in the bipartite graph results in a wide range of user interests,leading to the introduction of a large amount of noise during the propagation process.To this end,a new universal plug-in framework(UCBiG Plugin)is proposed to directly capture the group structures present in the item item co-occurrence graph,coarsen them into new nodes to construct a brand new user group node bipartite graph.Then,the strong proximity relationships between different items in these group structures are uti-lized to discover the potential high-order semantics of users.On three commonly used public datasets,two improved variants of the framework were applied for experimental evaluation,and it was found that the highest improved variants reached 9.51%and 8.89%,respectively.This proves that propagating collaboration signals on both user-item bipartite graphs and user-group node bipartite graphs can better capture rele-vant high-order connectivity information and be used for recommendation tasks.