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.
Description
Low-order tensor decompositions for social tagging recommendation
%0 Conference Paper
%1 Cai:2011:LTD:1935826.1935920
%A Cai, Yuanzhe
%A Zhang, Miao
%A Luo, Dijun
%A Ding, Chris
%A 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
%K decomposion recommender social tagging taggingsurvey tensor
%P 695--704
%R 10.1145/1935826.1935920
%T Low-order tensor decompositions for social tagging recommendation
%U http://doi.acm.org/10.1145/1935826.1935920
%X 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.
%@ 978-1-4503-0493-1
@inproceedings{Cai:2011:LTD:1935826.1935920,
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.},
acmid = {1935920},
added-at = {2011-05-18T16:19:56.000+0200},
address = {New York, NY, USA},
author = {Cai, Yuanzhe and Zhang, Miao and Luo, Dijun and Ding, Chris and Chakravarthy, Sharma},
biburl = {https://www.bibsonomy.org/bibtex/252a9e5fd121bf7be4fa8670cc93a7197/hotho},
booktitle = {Proceedings of the fourth ACM international conference on Web search and data mining},
description = {Low-order tensor decompositions for social tagging recommendation},
doi = {10.1145/1935826.1935920},
interhash = {414f80ad09d994af6f448446c04cd226},
intrahash = {52a9e5fd121bf7be4fa8670cc93a7197},
isbn = {978-1-4503-0493-1},
keywords = {decomposion recommender social tagging taggingsurvey tensor},
location = {Hong Kong, China},
numpages = {10},
pages = {695--704},
publisher = {ACM},
series = {WSDM '11},
timestamp = {2012-01-25T16:44:18.000+0100},
title = {Low-order tensor decompositions for social tagging recommendation},
url = {http://doi.acm.org/10.1145/1935826.1935920},
year = 2011
}