| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Cai, Y., Zhang, M., Luo, D., Ding, C. & Chakravarthy, S. | Low-order tensor decompositions for social tagging recommendation | 2011 | Proceedings of the fourth ACM international conference on Web search and data mining | inproceedings | DOIURL |
| Abstract: Social tagging recommendation is an urgent and useful enabling technology for Web 2.0. In this paper, we present a systematic study of low-order tensor decomposition approach that are specifically targeted at the very sparse data problem in tagging recommendation problem. Low-order polynomials have low functional complexity, are uniquely capable of enhancing statistics and also avoids over-fitting than traditional tensor decompositions such as Tucker and Parafac decompositions. We perform extensive experiments on several datasets and compared with 6 existing methods. Experimental results demonstrate that our approach outperforms existing approaches. | |||||
BibTeX:
@inproceedings{Cai:2011:LTD:1935826.1935920,
author = {Cai, Yuanzhe and Zhang, Miao and Luo, Dijun and Ding, Chris and Chakravarthy, Sharma},
title = {Low-order tensor decompositions for social tagging recommendation},
booktitle = {Proceedings of the fourth ACM international conference on Web search and data mining},
publisher = {ACM},
year = {2011},
pages = {695--704},
url = {http://doi.acm.org/10.1145/1935826.1935920},
doi = {http://dx.doi.org/10.1145/1935826.1935920}
}
|
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