Low-order tensor decompositions for social tagging recommendation.
In:
Proceedings of the fourth ACM international conference on Web search and data mining, Reihe WSDM '11, Seiten 695-704.
ACM, New York, NY, USA, 2011.
Yuanzhe Cai, Miao Zhang, Dijun Luo, Chris Ding und Sharma Chakravarthy.
[doi]
[Kurzfassung]
[BibTeX]
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.