Name
Abstract | ||
|---|---|---|
There are two primary approaches to collaborative filtering: memory-based and model-based. The traditional techniques fail to integrate with these two approaches and also can't fully utilize the tag features which data contains. Based on mining local information, this paper combines neighborhood method and matrix factorization technique. By taking fuller consideration of the tag features, we propose an algorithm named LTMF ( Local-Tag MF). After the real data validation, this model performs better than other state-of-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-28910-6_27 | Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering |
Field | DocType | Volume |
Data mining,Data validation,Collaborative filtering,Computer science,Matrix decomposition,Distributed computing | Conference | 163 |
ISSN | Citations | PageRank |
1867-8211 | 1 | 0.36 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deyuan Zheng | 1 | 1 | 0.36 |
| Huan Huo | 2 | 35 | 10.00 |
| Shangye Chen | 3 | 1 | 0.70 |
| Biao Xu | 4 | 1 | 0.36 |
| Liang Liu | 5 | 1 | 0.36 |