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The rapid development of location-based social networks (LBSNs) provides people with an opportunity of better understanding their mobility behavior which enables them to decide their next location. For example, it can help travelers to choose where to go next, or recommend salesmen the most potential places to deliver advertisements or sell products. In this paper, a method for recommending points of interest (POIs) is proposed based on a collaborative tensor factorization (CTF) technique. Firstly, a generalized objective function is constructed for collaboratively factorizing a tensor with several feature matrices. Secondly, a 3-mode tensor is used to model all users' check-in behaviors, and three feature matrices are extracted to characterize the time distribution, category distribution and POI correlation, respectively. Thirdly, each user's preference to a POI at a specific time can be estimated by using CTF. In order to further improve the recommendation accuracy, PCTF (Partitionbased CTF) is proposed to fill the missing entries of a tensor after clustering its every mode. Experiments on a real checkin database show that the proposed method can provide more accurate location recommendation.
Luan et al. (Sun,) studied this question.