social prediction, activity prediction, user modeling, social networks
A current trend for online social networks is to turn mobile. Mobile social networks directly reflect our real social life, and therefore are an important source to analyze and understand the underlying dynamics of human behaviors (activities). In this paper, we study the problem of activity prediction in mobile social networks. We present a series of observations in two real mobile social networks and then propose a method, ACTPred, based on a dynamic factor-graph model for modeling and predicting users’ activities. An approximate algorithm based on mean fields is presented to efficiently learn the proposed method. We deploy a real system to collect users’ mobility behaviors and validate the proposed method on two collected mobile datasets. Experimental results show that the proposed ACTPred model can achieve better performance than baseline methods.
Tsinghua University Press
Jibing Gong, Jie Tang, A. C. M. Fong. ACTPred: Activity Prediction in Mobile Social Networks. Tsinghua Science and Technology 2014, 19(03): 265-274.