TY  - CONF
AU  - Cai, Yuanzhe
AU  - Zhang, Miao
AU  - Luo, Dijun
AU  - Ding, Chris
AU  - Chakravarthy, Sharma
A2  - 
T1  - Low-order tensor decompositions for social tagging recommendation
T2  - Proceedings of the fourth ACM international conference on Web search and data mining
PB  - ACM
CY  - New York, NY, USA
PY  - 2011/
M2  - 
VL  - 
IS  - 
SP  - 695
EP  - 704
UR  - http://doi.acm.org/10.1145/1935826.1935920
M3  - 10.1145/1935826.1935920
KW  - tagging
KW  - taggingsurvey
KW  - recommender
KW  - social
KW  - decomposion
KW  - tensor
L1  - 
SN  - 978-1-4503-0493-1
N1  - Low-order tensor decompositions for social tagging recommendation
N1  - 
AB  - 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.
ER  -