期刊名称：Bulletin of the Technical Committee on Data Engineering
出版年度：2015
卷号：38
期号：2
出版社：IEEE Computer Society
摘要：With the development of location-based social networks, an increasing amount of individual mobilitydata accumulate over time. The more mobility data are collected, the better we can understand themobility patterns of users. At the same time, we know a great deal about online social relationshipsbetween users, providing new opportunities for mobility prediction. This paper introduces a novelty-seeking driven predictive framework for mining location-based social networks that embraces not only abunch of Markov-based predictors but also a series of location recommendation algorithms. The core ofthis predictive framework is the cooperation mechanism between these two distinct models, determiningthe propensity of seeking novel and interesting locations.
