Item enhanced graph collaborative network for collaborative filtering recommendation

Learning vector embeddings of users and items is the core of modern recommender systems. Recently the collaborative filtering recommender systems based on graph convolutional networks, which integrates the bipartite graph of user-item interaction into the embedding process, has achieved significant success. However, such feature as item-item interaction sequence is neglected in the bipartite graph, which limits the ability to model sequential orders for embedding of items. In this work, we propose a novel item-item interaction sequential graph to globally aggregate the hidden interactions sequence among all items. It is derived from the order of all user-item interactions and can give a supplement for user-item interaction modeling in CF. We also propose an item enhanced graph collaborative network (IEGCN) to mix item-item sequences with user-item interactions for collaborative filtering. We performed experiments on three open datasets, and IEGCN shows substantial improvements in recall and normalized discounted cumulative gain when compared with existing mainstream models. Further analysis verifies the importance of item-item sequence graph to improve the recommendation effect.