Matrix factorization for user behavior analysis of location-based social network

Abstract

The online social network services have been growing rapidly over the past few years, and allows user to share location information by the GPS enabled mobile device. The information about the location can reflect social characteristic and record behaviors tracks of users. For location information has an impact on user behavior analysis of location-based social network, this paper proposed a new matrix factorization for user behavior analysis in location-based social network. The matrix model is based on the information of the user and location. Considering that large data sets and the problem of matrix sparsity would significantly increase the time and space complexity, matrix factorization was introduced to alleviate the effect of data problems, combining with the key information about the user in a social network. In order to evaluate the proposed method, we crawl live data from the Foursquare social network. The experimental results show that the proposed method is effective in solving the problem of matrix sparsity which has large data sets, and improving the accuracy of user behavior analysis.