Integrating label propagation with graph convolutional networks for recommendation

Currently, graph convolutional networks (GCN) have achieved significant progress in recommender systems, due to its remarkable capability on representation learning and the ability to integrate complex auxiliary information. However, the graph convolution operation is prone to cause over-smoothing due to the use of the graph Laplacian operator, so that the node embeddings become very similar after the multi-layer graph convolution, which leads to a decrease in recommendation performance. The recently proposed model based on simplified GCN can relieve this issue to a certain extent; however, they still only design the model from the viewpoint of GCN. Inspired by the recent developments of label propagation algorithms (LPA), in this paper, we propose a new recommender model that unifies graph convolutional networks and label propagation algorithms. Specifically, we utilize the GCN to build a basic recommendation prediction model, and unify the LPA to provide regularization of training edge weights, which has been proven to effectively alleviate the over-smoothing problem. In addition, we introduce an attention network to capture the attention weight of each user-item pair, which takes into account the fact that users attach different degrees of importance to various relationships of items. Extensive experiments on three real-world datasets demonstrate that the proposed algorithm has a significant improvement over other state-of-the-art recommendation algorithms.