Cai, Y.; Zhang, M.; Luo, D.; Ding, C. & Chakravarthy, S.
(2011):
Low-order tensor decompositions for social tagging recommendation.
In: Proceedings of the fourth ACM international conference on Web search and data mining,
New York, NY, USA.
[Volltext]
[Kurzfassung] [BibTeX][Endnote]
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.
@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},
series = {WSDM '11},
publisher = {ACM},
address = {New York, NY, USA},
year = {2011},
pages = {695--704},
url = {http://doi.acm.org/10.1145/1935826.1935920},
doi = {10.1145/1935826.1935920},
isbn = {978-1-4503-0493-1},
keywords = {tagging, taggingsurvey, recommender, social, decomposion, tensor},
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.}
}
%0 = inproceedings
%A = Cai, Yuanzhe and Zhang, Miao and Luo, Dijun and Ding, Chris and Chakravarthy, Sharma
%B = Proceedings of the fourth ACM international conference on Web search and data mining
%C = New York, NY, USA
%D = 2011
%I = ACM
%T = Low-order tensor decompositions for social tagging recommendation
%U = http://doi.acm.org/10.1145/1935826.1935920