Learning Implicit Relations for Collaborative Filtering via Optimal Transport

Jingwen Wang, Chuangguang Huang, Jiabi Zheng, Yuguang Yan

Abstract: Recommender systems seek to present candidate items for users based on their potential preferences. Beneficial from the powerful ability of the graph model to leverage structural information, the graph based approach has drawn much attention in the community of recommendation. However, existing graph based methods usually use interaction data or some heuristic rules to construct the graph, without considering implicit relations involved in features and higher-order dependencies of users and items. In this paper, we propose a novel graph collaborative filtering method based on the theory of optimal transport, which is devoted to extracting features and dependencies of users and items to reveal implicit relations between them. Specifically, we model users and items as two distributions and propose an optimal transport model to characterize strong relations between them. By incorporating feature and dependency information of users and items, we learn implicit relations and features of users and items in a unified learning model. Based on the learned relations and original interaction data, we design a dual-stream graph convolutional network to enhance the representation learning for users and items, boosting the performance of recommendation. The experimental results on two real-world datasets demonstrate the effectiveness of our method. We also conduct ablation studies to evaluate the effects of the learned features and relations obtained from our optimal transport model.

External IDs:dblp:conf/ijcnn/WangHZY25