Title: A social tag recommendation method alleviating cold start based on probabilistic graphical model

Authors: Qian Xiao; Haitao Xie

Addresses: Beijing Institute of Graphic Communication, Beijing, 102600, China ' School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China

Abstract: Existing social tag recommendation methods suffer from the cold start problem of tags. To this end, a PageRank-Like tags recommendation (MRF-rank) method is proposed. MRF-rank can adjust recommending chances of tags to alleviate cold start. We detect clique-group within communities, which are considered as basic units showing usage patterns of taggings. We present an ensemble Markov random field (eMRF) model to learn the usage patterns of high quality taggings and then estimate qualities of taggings with few usage records. MRF-rank is proposed, which represents estimated qualities as the weights of tag-resource edges. By specifying a preference vector of target user and resource, MRF-rank spreads the weights among vertices, and then generates tags recommendation based on the weights rank of tags. The experimental results comparing existing methods show that MRF-rank achieves better recommendations in terms of recall and precision.

Keywords: cold start; social tag recommendation; probabilistic graphical modelling; cliques; tag usage patterns; ensemble MRF; Markov random field; eMRF; high quality taggings.

DOI: 10.1504/IJES.2015.069994

International Journal of Embedded Systems, 2015 Vol.7 No.2, pp.162 - 169

Received: 23 Apr 2014
Accepted: 21 May 2014
Published online: 22 Jun 2015 *

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