Graph Neural Networks for Social Recommendation

Wenqi Fan,Yuan He,Yao Ma,Dawei Yin,Qing Li,Jiliang Tang,Eric Zhao

doi:10.1145/3308558.3313488

Wenqi Fan, Yuan He + Show 5 more

Open Access

https://doi.org/10.1145/3308558.3313488

Copy DOI

Abstract

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec.

Highlights

The exploitation of social relations for recommender systems has attracted increasing attention in recent years [18, 28, 30]
Recent years have witnessed great developments in deep neural network techniques for graph data [15]. These deep neural network architectures are known as Graph Neural Networks (GNNs) [5, 10, 19], which have been proposed to learn meaningful representations for graph data
We aim to build social recommender systems based on graph neural networks

Summary

Introduction

The exploitation of social relations for recommender systems has attracted increasing attention in recent years [18, 28, 30]. Recent years have witnessed great developments in deep neural network techniques for graph data [15] These deep neural network architectures are known as Graph Neural Networks (GNNs) [5, 10, 19], which have been proposed to learn meaningful representations for graph data. Learning representations of items and users is the key to build social recommender systems. Given their advantages, GNNs provide unprecedented opportunities to advance social recommendation

Objectives

Results

Conclusion