Low-order tensor decompositions for social tagging recommendation
Y. Cai, M. Zhang, D. Luo, C. Ding, und S. Chakravarthy. Proceedings of the fourth ACM international conference on Web search and data mining, Seite 695--704. New York, NY, USA, ACM, (2011)
Zusammenfassung
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.
Beschreibung
Low-order tensor decompositions for social tagging recommendation
