Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

Abstract

Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. However, most existing models assume that social effects from friend users are static and under the forms of constant weights or fixed constraints. To relax this strong assumption, in this paper, we propose dual graph attention networks to collaboratively learn representations for two-fold social effects, where one is modeled by a user-specific attention weight and the other is modeled by a dynamic and context-aware attention weight. We also extend the social effects in user domain to item domain, so that information from related items can be leveraged to further alleviate the data sparsity problem. Furthermore, considering that different social effects in two domains could interact with each other and jointly influence user preferences for items, we propose a new policy-based fusion strategy based on contextual multi-armed bandit to weigh interactions of various social effects. Experiments on one benchmark dataset and a commercial dataset verify the efficacy of the key components in our model. The results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art social recommendation methods.

Accepted by WWW2019 as a full paper with oral presentation

References in corpus (4)

• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• DeepInf: Social Influence Prediction with Deep Learning
• Item Silk Road: Recommending Items from Information Domains to Social Users
• Multiresolution Graph Attention Networks for Relevance Matching

Cited by in corpus (14)

• A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
• A Survey of Graph Neural Networks for Social Recommender Systems
• Federated Social Recommendation with Graph Neural Network
• ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation
• Attention-based graph neural networks: a survey
• Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks
• Multi-Behavior Graph Neural Networks for Recommender System
• Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network
• GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation
• Stochastic Graph Neural Networks
• Counterfactual Learning on Graphs: A Survey
• Recommending on graphs: a comprehensive review from a data perspective
• Unify Local and Global Information for Top-$N$ Recommendation
• Extracting Attentive Social Temporal Excitation for Sequential Recommendation