Abstract: Location-Based Social Networks (LBSN) provides users with location-based services,allowing mobile users to share their location and location-related information in social networks.The research of check-in prediction has become an important and very challenging task in LBSN.Most of the current prediction techniques mainly focus on user-centered check-in studies,while few researches are based on POI-centered.This paper focused on the check-in prediction of POI-centered.Due to the extreme sparseness of data,it is difficult to use the traditional model to dig out users’ potential check-in pattern from data.To solve the problem of prediction based on POI-centered,this paper proposed a novel network model(TSWNN) combining factorization machine and deep learning.This model fuses temporal features,spatial features and weather features,takes advantage of the idea of factorization machine to deal with high dimensional sparse vectors and applies fully-connected hidden layer to the model to dig out users’ potential check-in pattern and predict users’ check-in behavior on specific point of interest.The experimental results on two classical LBSN datasets(Gowalla and Brightkite) show the superior performance of the proposed model.

Key words: Check-in prediction, Deep learning, Factorization machine, Point of interest