Name
Abstract | ||
|---|---|---|
Recommender systems aim to accurately and actively provide users with potentially interesting items (products, information or services). Deep reinforcement learning has been successfully applied to recommender systems, but still heavily suffer from data sparsity and cold-start in real-world tasks. In this work, we propose an effective way to address such issues by leveraging the pervasive social n... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TKDE.2020.3012346 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | DocType | Volume |
Social network services,Learning (artificial intelligence),Recommender systems,Machine learning,Task analysis,Estimation,Standards | Journal | 34 |
Issue | ISSN | Citations |
5 | 1041-4347 | 0 |
PageRank | References | Authors |
0.34 | 39 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Yu | 1 | 26 | 7.96 |
| Z Wang | 2 | 80 | 11.32 |
| Wenjie Li | 3 | 368 | 59.74 |
| Hongbin Pei | 4 | 16 | 4.25 |
| Quanyu Dai | 5 | 28 | 5.28 |