Name
Abstract | ||
|---|---|---|
Modern recommendation systems use embedding for secondary applications and implicit feedback data for learning. In recent years, collaborative metric learning (CML), a method that can precisely capture the relationship between users and items, has been developed for the first requirement. However, CML with implicit feedback data suffers from noisy-label issues. This is mainly because CML and the r... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICDMW53433.2021.00038 | 2021 International Conference on Data Mining Workshops (ICDMW) |
Keywords | DocType | ISSN |
Recommender systems,Collaborative filtering,Implicit feedback,Noisy label,Metric learning | Conference | 2375-9232 |
ISBN | Citations | PageRank |
978-1-6654-2427-1 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryo Matsui | 1 | 0 | 0.34 |
| Taketo Naito | 2 | 0 | 0.34 |
| Suguru Yaginuma | 3 | 0 | 0.34 |
| Kazuhide Nakata | 4 | 0 | 0.68 |