KGCNA: Knowledge Graph Collaborative Neighbor Awareness Network for Recommendation

Abstract

Knowledge graph (KG) is increasingly important in improving recommendation performance and handling item cold-start. A recent research hotspot is designing end-to-end models based on information propagation schemes. However, existing these methods do not highlight key collaborative signals hidden in user-item bipartite graphs, which leads to two problems: (1) the collaborative signal of user collaborative neighbors is not modeled and (2) the incompleteness of KG and the behavioral similarity of item collaborative neighbors are not considered. In this paper, we design a new model called Knowledge Graph Collaborative Neighbor Awareness network (KGCNA) in order to resolve the above problems. KGCNA models the top-k collaborative neighbors of users and items to extract the collaborative preference of the user's top-k collaborative neighbors, the missing attributes of items, and the behavioral similarity of the item's top-k collaborative neighbors, respectively. At the same time, KGCNA designs a novel information aggregation method, which adopts different aggregation methods for users and items to capture the user's item-based behavior preference and the item's long-distance knowledge association in KG, respectively. Furthermore, KGCNA uses an information-gated aggregation mechanism to extract discriminative signals to better study user behavior intent. Experimental results on three benchmark datasets demonstrate that KGCNA significantly improves over state-of-the-art techniques such as CKAN, KGIN, and KGAT.