Name
Abstract | ||
|---|---|---|
The recommender system is of great significance to alleviate information overload. The rise of online social networks leads to a promising direction-social recommendation. By injecting the interaction influence among social users, recommendation performance has been further improved. Successful as they are, we argue that most social recommendation methods are still not sufficient to make full use of social network information. Existing solutions typically either considered only the local neighbors or treat neighbors' information equally, even or both. However, few studies have attempted to solve these social recommendation problems jointly from both the perspective of social depth and social strength. Recently, graph convolutional neural networks have shown great potential in learning graph data by modeling the information propagation and aggregation process. Thus, we propose an attention-based social aggregation neural networks (abbreviated as SAN) model to build a recommendation system. Different from previous work, our proposed SAN model simulates the recursive social aggregation process to spread the global social influence, and simultaneously introduces social attention mechanism to incorporate the heterogeneous influences for better model user embedding. Instead of a shallow linear interaction function, we adopt multi-layer perception to model the complex user-item interaction. Extensive experiments on two real-world datasets show the effectiveness of our proposed model SAN, and further analysis verifies the generalization and flexibility of the model. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1002/int.22694 | INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS |
Keywords | DocType | Volume |
attention, graph convolutional networks, social influence, social interaction, social recommendation | Journal | 37 |
Issue | ISSN | Citations |
6 | 0884-8173 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nan Jiang | 1 | 11 | 2.30 |
| Li Gao | 2 | 46 | 21.48 |
| Fuxian Duan | 3 | 0 | 0.34 |
| Jie Wen | 4 | 0 | 0.34 |
| Tao Wan | 5 | 0 | 1.01 |
| Honglong Chen | 6 | 0 | 1.01 |