A deeper graph neural network for recommender systems

Abstract Interaction data in recommender systems are usually represented by a bipartite user–item graph whose edges represent interaction behavior between users and items. The data sparsity problem, which is common in recommender systems, is the result of insufficient interaction data in the link prediction on graphs. The data sparsity problem can be alleviated by extracting more interaction behavior from the bipartite graph, however, stacking multiple layers will lead to over-smoothing, in which case, all nodes will converge to the same value. To address this issue, we propose a deeper graph neural network in this paper that can predict links on a bipartite user–item graph using information propagation. An attention mechanism is introduced to our method to address the problem that variable size inputs for each node on a bipartite graph. Our experimental results demonstrate that our proposed method outperforms five baselines, suggesting that the interactions extracted help to alleviate the data sparsity problem and improve recommendation accuracy.