Low-order tensor decompositions for social tagging recommendation

, , , , und . Proceedings of the fourth ACM international conference on Web search and data mining, Seite 695--704. New York, NY, USA, ACM, (2011)


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.


Low-order tensor decompositions for social tagging recommendation

Links und Ressourcen

Suchen auf:

Kommentare und Rezensionen  

Es gibt bisher keine Rezension oder Kommentar. Sie können eine schreiben!


Zitieren Sie diese Publikation