Name
Abstract | ||
|---|---|---|
Recommender systems for online broadcasting become important as the number of channels has been increasing. In online broadcasting, to provide accurate recommendation, recommender systems should take time factors as well as users’ condition into account, but the conventional systems don’t. This paper proposes a real-time recommender system for online broadcasting called RecTime which considers time factors and preferences simultaneously. Specifically, RecTime employs a 4-d tensor factorization, which considers two more dimensions regarding the time factors, while typical collaboriative filtering methods only consider two dimensions, users and items. By factorizing the 4-d tensor, the system naturally identifies the recommendation time and the items at the same time. In our experiments on real-world data, RecTime properly models users’ watching patterns and significantly outperforms previous methods in terms of the accuracy on the recommendation time as well as the items. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.ins.2017.04.038 | Information Sciences |
Keywords | Field | DocType |
Recommendation,TV show,Deciding recommendation timing | Recommender system,Broadcasting,Information retrieval,Tensor,Computer science,Communication channel,Filter (signal processing),Artificial intelligence,Tensor factorization,Multimedia,Machine learning | Journal |
Volume | ISSN | Citations |
409 | 0020-0255 | 5 |
PageRank | References | Authors |
0.40 | 34 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoojin Park | 1 | 5 | 0.40 |
| Jinoh Oh | 2 | 303 | 15.32 |
| Hwanjo Yu | 3 | 1715 | 114.02 |