HN-GCCF: High-order neighbor-enhanced graph convolutional collaborative filtering

Abstract

Graph convolutional collaborative filtering (CF) has recently made remarkable progress. Its great success is attributed to the embedding propagation mechanism of graph convolutional networks (GCNs), which can capture the collaborative signals hidden in the high-order neighbors by stacking multiple embedding propagation layers (i.e., graph convolution layers). However, we argue that existing GCN-based CF models are insufficient for exploiting collaborative filtering effects since their embedding propagation schemes and sampling strategies cannot make full use of the rich collaborative signals of the high-order neighbors. In this work, we propose two high-order neighbor-enhanced (HN) strategies from two different perspectives, i.e., embedding propagation (EP) and positive user–item pair (PP) construction. Specifically, the EP-based HN strategy designs a new embedding propagation scheme, i.e., a high-order graph convolution operation, to alleviate the excessive decay of high-order neighbor information. The PP-based HN strategy mines high-quality positive user–item pairs to enhance model training by utilizing users’ high-order neighbors as potentially positive samples. Besides, we present the high-order neighbor-enhanced graph convolutional collaborative filtering (HN-GCCF) model, equipped with the above two HN strategies. Extensive experiments demonstrate that HN-GCCF significantly outperforms state-of-the-art GCN-based CF models in terms of both effectiveness and efficiency.