Stacked Mixed-Order Graph Convolutional Networks for Collaborative Filtering
Hengrui Zhang, Julian McAuley
Abstract
Graph-based recommendation algorithms treat user-item interactions as bipartite graphs, based on which low-dimensional vector representations of users and items seek to preserve the relationships among them. Previous methods usually capture users' preferences by directly learning first-order neighborhood patterns for each node, which limits their ability to exploit the similarity between two distant users/items as well as a user's preferences toward distant items. To address this potential weakness, in this paper, we propose SMOG-CF (Stacked Mixed-Order Graph Convolutional Networks for Collaborative Filtering), a GCN-based framework that can directly capture high-order connectivity among nodes. Instead of implicitly capturing high-order connectivity through embedding propagation, SMOG-CF facilitates ‘path-level’ information propagation between neighboring nodes at any order. The matrix form of our embedding propagation formulas yields a model that is easy to deploy and can be extended to a general framework by adopting various information construction and aggregation equations. Experiments on several datasets of varying scale demonstrate the efficacy of our model.