title: Followee recommendation in microblog using matrix factorization model with structural regularization.
abstract: Microblog that provides us a new communication and information sharing platform has been growing exponentially since it emerged just a few years ago. To microblog users, recommending followees who can serve as high quality information sources is a competitive service. To address this problem, in this paper we propose a matrix factorization model with structural regularization to improve the accuracy of followee recommendation in microblog. More specifically, we adapt the matrix factorization model in traditional item recommender systems to followee recommendation in microblog and use structural regularization to exploit structure information of social network to constrain matrix factorization model. The experimental analysis on a real-world dataset shows that our proposed model is promising.
authors.affiliation: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ; Computer Science Department, Southeast University Chengxian College, Nanjing 210088, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Yu
authors.affiliation: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ; Information Science Department, Pennsylvania State University, Great Valley, Malvern, PA 16802, USA.
authors.forename: Robin G
authors.initials: RG
authors.lastname: Qiu
mesh_terms: D000465:Algorithms; D003195:Computer Communication Networks; D006801:Humans; D008962:Models, Theoretical