| Autor: |
Yi Yang, Lixin Han, Zhinan Gou, Baobin Duan, Jun Zhu, Hong Yan |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 6, Pp 64142-64152 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2018.2877764 |
| Popis: |
In order to address data sparsity, missing value, and over-fitting problems in a social tagging system, a coupled matrix and tensor factorization (CMTF) method named Tagrec-CMTF for tag recommendation is proposed in this paper. In the CMTF method, we decompose the tag-item-user tensor joint with tag graph and two auxiliary matrices by using the CMTF, optimize the learning parameters with an alternating direction method of multipliers algorithm, and recommend the tag according to the predicted tensor. Our algorithm infuses the homogeneous and heterogeneous information of the tag and provides good prediction performance. Experiment results show that Tagrec-CMTF outperforms existing methods that do not utilize the homogeneous and heterogeneous information of the tag simultaneously. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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