Name
Abstract | ||
|---|---|---|
With the popularity of social network applications, more and more recommender systems utilize trust relationships to improve the performance of traditional recommendation algorithms. Social-network-based recommendation algorithms generally assume that users with trust relations usually share common interests. However, the performance of most of the existing social-network-based recommendation algorithms is limited by the coarse-grained and sparse trust relationships. In this paper, we propose a network representation learning enhanced recommendation algorithm. Specifically, we first adopt a network representation technique to embed social network into a low-dimensional space, and then utilize the low-dimensional representations of users to infer fine-grained and dense trust relationships between users. Finally, we integrate the fine-grained and dense trust relationships into the matrix factorization model to learn user and item latent feature vectors. The experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2916186 | IEEE ACCESS |
Keywords | Field | DocType |
Network representation learning, recommendation algorithm, matrix factorization, social network | Recommender system,Feature vector,Social network,Computer science,Matrix decomposition,Popularity,Algorithm,Network representation learning | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Wang | 1 | 601 | 84.65 |
| Yonghong Yu | 2 | 36 | 7.05 |
| Haiyan Gao | 3 | 0 | 0.34 |
| Li Zhang | 4 | 298 | 40.28 |
| Y. Y. Cao | 5 | 266 | 55.94 |
| Lin Mao | 6 | 8 | 1.28 |
| Kaiqi Dou | 7 | 0 | 0.34 |
| Wenye Ni | 8 | 0 | 0.34 |