Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks

TL;DR

This paper introduces SR-HGNN, a hierarchical graph neural network framework that effectively captures multi-type user-item interactions and global social context, significantly improving social recommendation performance.

Contribution

It proposes a novel relation-aware graph neural network and a mutual information-based social relation encoder to enhance social recommendation models.

Findings

SR-HGNN outperforms state-of-the-art methods on three benchmarks.

The relation-aware graph neural network effectively models cross-type interactions.

The mutual information encoder captures global social context.

Abstract

Social recommendation which aims to leverage social connections among users to enhance the recommendation performance. With the revival of deep learning techniques, many efforts have been devoted to developing various neural network-based social recommender systems, such as attention mechanisms and graph-based message passing frameworks. However, two important challenges have not been well addressed yet: (i) Most of existing social recommendation models fail to fully explore the multi-type user-item interactive behavior as well as the underlying cross-relational inter-dependencies. (ii) While the learned social state vector is able to model pair-wise user dependencies, it still has limited representation capacity in capturing the global social context across users. To tackle these limitations, we propose a new Social Recommendation framework with Hierarchical Graph Neural Networks (SR-HGNN). In particular, we first design a relation-aware reconstructed graph neural network to inject the cross-type collaborative semantics into the recommendation framework. In addition, we further augment SR-HGNN with a social relation encoder based on the mutual information learning paradigm between low-level user embeddings and high-level global representation, which endows SR-HGNN with the capability of capturing the global social contextual signals. Empirical results on three public benchmarks demonstrate that SR-HGNN significantly outperforms state-of-the-art recommendation methods. Source codes are available at: https://github.com/xhcdream/SR-HGNN.