SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation

TL;DR

This paper introduces SocialGCN, a graph convolutional network model for social recommendation that captures how user preferences are influenced by social diffusion, improving recommendation accuracy especially in data-sparse scenarios.

Contribution

The paper proposes a novel GCN-based social recommendation model that models social diffusion of preferences and is flexible without requiring user or item features.

Findings

Outperforms existing social recommendation models on real datasets.

Effectively models preference diffusion in social networks.

Demonstrates robustness without user or item features.

Abstract

Collaborative Filtering (CF) is one of the most successful approaches for recommender systems. With the emergence of online social networks, social recommendation has become a popular research direction. Most of these social recommendation models utilized each user's local neighbors' preferences to alleviate the data sparsity issue in CF. However, they only considered the local neighbors of each user and neglected the process that users' preferences are influenced as information diffuses in the social network. Recently, Graph Convolutional Networks~(GCN) have shown promising results by modeling the information diffusion process in graphs that leverage both graph structure and node feature information. To this end, in this paper, we propose an effective graph convolutional neural network based model for social recommendation. Based on a classical CF model, the key idea of our proposed model is that we borrow the strengths of GCNs to capture how users' preferences are influenced by the social diffusion process in social networks. The diffusion of users' preferences is built on a layer-wise diffusion manner, with the initial user embedding as a function of the current user's features and a free base user latent vector that is not contained in the user feature. Similarly, each item's latent vector is also a combination of the item's free latent vector, as well as its feature representation. Furthermore, we show that our proposed model is flexible when user and item features are not available. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model.