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

25 Mar 2019  ·  Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen ·

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Recommendation Systems Epinions DANSER MAE 0.7781 # 1
RMSE 1.0268 # 1
Recommendation Systems WeChat DANSER AUC 0.8165 # 1
P@10 0.0823 # 1


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