Deep adaptive collaborative graph neural network for social recommendation

Most graph convolutional network (GCN)-based social recommendation frameworks fuse social links with user-item interactions to enrich user representations, which alleviate the cold-start problem and data sparsity problem. However, GCN-based recommender systems still suffer from two limitations. First, Excessive reliance on social graphs to extract user interests for rating predictions is unreliable due to social inconsistency. Second, GCN-based models suffer from over-smoothing problems, node embeddings become more similar when going deeper to enable larger receptive fields. To address the two aforementioned problems simultaneously, we propose a Deep Adaptive Collaborative Graph Neural Network for Social Recommendation (DUI-SoRec). First, the graph generation module decomposes the user-item interaction to generate two subgraphs: an u2u graph and an i2i graph. Secondly, the graph learning module utilizes a deep adaptive graph neural network to learn user and item embeddings on the two subgraphs and the existing social graph, while solving the over -smoothing problem. Finally, we designed a refined fusion module to aggregate the social graph and u2u graph to address the social inconsistency. We conducted extensive experiments on four real-world datasets and the results demonstrate the model's effectiveness.

Automated summary

In this paper, we propose a Deep Adaptive Collaborative Graph Neural Network for Social Recommendation (DUI-SoRec), which addresses the issues of social inconsistency and over-smoothing in GCN-based recommender systems. By generating two subgraphs and utilizing a deep adaptive graph neural network, the model learns user and item embeddings effectively. The model's effectiveness is demonstrated through extensive experiments on real-world datasets.