User-Centric Location Prediction in Mobile Social Networks: A Factor Graph Learning Approach

摘要

Recently, location prediction has attracted considerable research effort because of the popularity of location- based services, such as mobile advertising and recommendations. With the unprecedented proliferation of mobile social networks, we are able to use location service to bridge the online and offline worlds. Different from existing studies, in this paper we promote a user-centric location prediction approach by leveraging a user's local mobile social information without involving other users' location privacy. We propose a factor graph learning model that integrates not only user's social and network information, but also the correlations between user's locations into a unified framework. Furthermore, we use ReliefF algorithm to select user-specific significant features for location prediction and define the measure of location entropy to study the similarity between location, network status and social behavior. We conduct extensive experiments using a real-world dataset, which shows that our model performs much better in location prediction compared with several classic methods.