@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}, address = {New York, NY, USA}, author = {Cai, Yuanzhe and Zhang, Miao and Luo, Dijun and Ding, Chris and Chakravarthy, Sharma}, booktitle = {Proceedings of the fourth ACM international conference on Web search and data mining}, doi = {10.1145/1935826.1935920}, interhash = {414f80ad09d994af6f448446c04cd226}, intrahash = {52a9e5fd121bf7be4fa8670cc93a7197}, isbn = {978-1-4503-0493-1}, location = {Hong Kong, China}, numpages = {10}, pages = {695--704}, publisher = {ACM}, series = {WSDM '11}, title = {Low-order tensor decompositions for social tagging recommendation}, url = {http://doi.acm.org/10.1145/1935826.1935920}, year = 2011 }