Latent Co-interests’ Relationship Prediction

Feng Tan; Li Li; Zheyu Zhang; Yunlong Guo

doi:10.1109/TST.2013.6574676

Tsinghua Science and Technology 2013, 18(4): 379-386 https://doi.org/10.1109/TST.2013.6574676

Open Access | Issue | Published: 05 August 2013

Latent Co-interests’ Relationship Prediction

Show Author's Information Hide Author's Information Feng Tan, Li Li(

), Zheyu Zhang, Yunlong Guo

Department of Computer and Information Science, Southwest University, Chongqing 400715, China

Keywords:

social network, linking prediction, node similarity, factor graph model

Cite this article:

Tan F, Li L, Zhang Z, et al. Latent Co-interests’ Relationship Prediction. Tsinghua Science and Technology, 2013, 18(4): 379-386. https://doi.org/10.1109/TST.2013.6574676

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Abstract Full text About this article

Abstract

With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests’ relationship, we not only consider users’ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model (I-FGM) to incorporate these factors. Experiments on two data sets (bookmarking and music network) demonstrate that this predictive method can achieve better results than the other three methods (ANN, NB, and SVM).

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About this article

Latent Co-interests’ Relationship Prediction

Show Author's information Hide Author's Information Feng Tan, Li Li(

), Zheyu Zhang, Yunlong Guo

Department of Computer and Information Science, Southwest University, Chongqing 400715, China

Abstract

Keywords: social network, linking prediction, node similarity, factor graph model

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Received: 28 June 2013

Accepted: 28 June 2013

Published: 05 August 2013

Issue date: August 2013

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61170192) and the Natural Science Foundations of Municipality of Chongqing (No. CSTC2012JJB40012).