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

By Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen. The Web Conference, 2019

362 citations recorded by the source corpus.

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 users' 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.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/03ed44b85886a7a95d1533fb1d1a142e60ae292c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Hongzhi Yin
3. Xiang Wang
4. Yong Li
5. Junliang Yu
6. Meng Wang
7. Chen Gao
8. Le Wu

In Information Systems

1. Xiangnan He
2. Hongzhi Yin
3. Xiang Wang
4. Meng Wang
5. Junliang Yu
6. Le Wu
7. Yong Li
8. Chen Gao

In Management Science and Operations Research

1. Xiang Wang
2. Meng Wang
3. Xiangnan He
4. Le Wu
5. Kun Zhang
6. Richang Hong
7. Hongzhi Yin
8. Fei Sun

Citations in the corpus

• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, methodology): “Then, the methods [43, 61, 82, 116, 133, 155, 197, 198, 201, 217, 225, 226] that model both social network and user interactions with GNNs become the mainstream in GNN-based social recommendation.”
• DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation (2020, background): “some recently works that also leverage the graph neural networks for social recommendation [10], [44].”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, methodology): “Instead of performing GNNs on the user-user social graph, researchers have also considered jointly modeling the social diffusion process in the social network and the interest diffusion process in the user-item graph with heterogeneous GNN based models [158], [161], [162], [163],”
• A Survey of Graph Neural Networks for Social Recommender Systems (2022, methodology): “Therefore, almost all existing works on SocialRS have leveraged the attention mechanism in their GNN encoders [1, 2, 5, 6, 9, 10, 10, 15–18, 26, 27, 30–32, 36, 38, 40, 42, 44, 51–53, 59, 63, 65, 66, 70, 73, 80–82, 84, 89, 91, 92, 100, 101, 104, 106, 109, 110, 112, 114, 115, 117, 121, 128, 131, 134].”
• Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (2021, background): “[42] propose a dual graph attention network to collaboratively learn representations for two-fold social effects.”
• CoCoGUM: Contextual Code Summarization with Multi-Relational GNN on UMLs (2020, background): “Due to the strong ability of learning from graph data, GNNs have achieve a great success in many tasks like learning chemistry rule and phenomenon [31], recommender systems [32], document classification [33], traffic predication [34], etc.”
• Graph Neural Networks in Recommender Systems: A Survey (2020, background): “DANSER [68] WWW 2019 √ √ Social + Item-to-item graph variant of GAT”