Self-Attentive Graph Convolution Network With Latent Group Mining and Collaborative Filtering for Personalized Recommendation

The remarkable progress of machine learning has led to some state-of-the-art algorithms in personalized recommendation. Previous recommendation algorithms generally learn users' and items' representations based on a user-item rating matrix. However, these methods only consider a user's own preference, but ignore the influence of the user's social circles. In this paper, we propose a novel recommendation algorithm, Self-Attentive Graph Convolution Network with Latent Group Mining and Collaborative Filtering, which consists of Latent Group Mining (LGM) module, Collaborative Embedding (CE) module and Self-Attentive Graph Convolution (SAGC) module. The LGM module analyzes users' social circles by exploring their latent groups and generates group embedding for users and items. The CE module uses a graph embedding method to provide semantic collaborative embedding for users and items. The SAGC module fuses users' (items') collaborative embedding and group embedding by a self-attentive graph convolution network to learn their fine-grained representations for rating prediction. We conduct experiments on different real-world datasets, which validates that our algorithm outperforms the state-of-the-art algorithms.

Automated summary

This paper proposes a novel recommendation algorithm that combines self-attentive graph convolution network, latent group mining, and collaborative filtering. Experimental results show that the algorithm outperforms the state-of-the-art algorithms on real-world datasets.