User Follow Prediction of Microblog Based on the Activeness and Interest Similarity

Many microblog fans have formed the basis of microblog information dissemination and diffusion, so accurate prediction and attracting more potential customers to follow microblogger becomes very necessary. By employing interpersonal relationship network of microblog fans, this study integrates microblogger popularity and user activeness into interest similarity in order to explain user follow predictor and propose user follow prediction models. Support vector machine (SVM) is used to train this model. Open data from Tencent microblog in KDD Cup 2012 prove that the proposed prediction model has higher prediction accuracy and stability. Key words: Microblog; User follow; Microblogger popularity; User activeness; Interest similarity; Prediction model

[1]  Xing Zhao.  Scorecard with Latent Factor Models for User Follow Prediction Problem , 2012 .

[2]  Luxu Zhang,et al.  Method of Recommend Microblogging Based on User Model , 2012, 2012 IEEE 12th International Conference on Computer and Information Technology.

[3]  Chien-Lung Hsu,et al.  Effect of Commitment and Trust towards Micro-blogs on Consumer Behavioral Intention: A Relationship Marketing Perspective , 2010, Int. J. Electron. Bus. Manag..

[4]  Yuan Rui.  Bursty Propagation Model for Incidental Events in Blog Networks , 2009 .

[5]  Zhao Li,et al.  Bursty Propagation Model for Incidental Events in Blog Networks , 2009 .

[6]  Robert A. Pongsajapan.  Liminal entities: Identity, governance, and organizations on Twitter , 2009 .

[7]  Martin Ebner,et al.  Microblogging - more than fun? , 2008 .

[8]  Dirk Riehle,et al.  Micro-Blogging Adoption in the Enterprise: An Empirical Analysis , 2011, Wirtschaftsinformatik.

[9]  Weigang Li,et al.  Logical Model of Relationship for Online Social Networks and Performance Optimizing of Queries - WISE 2012 Challenge - T1: Performance Track Scalability Winner , 2012, WISE.

[10]  Stuart J. Barnes,et al.  Modeling Use Continuance Behavior in Microblogging Services: The Case of Twitter , 2011, J. Comput. Inf. Syst..

[11]  Paul Mcfedries,et al.  Technically Speaking , 2007, IEEE Spectrum.

[12]  Analía Amandi,et al.  Topology-Based Recommendation of Users in Micro-Blogging Communities , 2012, Journal of Computer Science and Technology.

[13]  Yaobin Lu,et al.  Perceived Interactivity: Exploring Factors Affecting Micro-Blogging Service Satisfaction and Continuance Intention , 2010, PACIS.

[14]  Yunwen Chen,et al.  Context-aware Ensemble of Multifaceted Factorization Models for Recommendation Prediction in Social Networks , 2021, ArXiv.

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  Diyi Yang,et al.  Combining Factorization Model and Additive Forest for Collaborative Followee Recommendation , 2012 .

[17]  Dirk Riehle,et al.  Modeling Microblogging Adoption in the Enterprise , 2009, AMCIS.