Zhi-fang Liao This email address is being protected from spambots. You need JavaScript enabled to view it.1, Fei Cai1, Miao Zhang2, Zhi-ning Liao3 and Yan Zhang4

1School of Software, Central South University, Changsha, Hunan, P.R. China
2Department of Computer Science, University of Texas at Arlington, Arlington, TX, USA
3Faculty of Engineering, Science & The Built Environment, London South Bank University, London, UK
4Institute of Human Development, The University of Manchester, UK


 

Received: March 6, 2013
Accepted: September 22, 2014
Publication Date: December 1, 2014

Download Citation: ||https://doi.org/10.6180/jase.2014.17.4.03  


ABSTRACT


The tripartite tensor decomposition (TTD) model reveals the latent relationship among items, tags and users in social tagging systems in terms of a low order tensor obtained from the high-index sparse data space with the tensor dimensionality reduction technique. The Tripartite decomposition recommendation algorithms can produce high quality recommendations, but have to undergo expensive tensor decomposition steps when new users, new tags, or new items come in, which is significant in light of the tremendous growth in numbers of users, tags and items. In this paper, we present fold-in algorithms for Tripartite tensor decomposition to deal with the new users problem. We evaluate the fold-in algorithms experimentally on several datasets and the results demonstrate the effectiveness of the algorithm.


Keywords: Recommender System, Tripartite Tensor Decomposition, Fold-in, Social Network


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