Partition-based collaborative tensor factorization for POI recommendation

The rapid development of location-based social networks U+0028 LBSNs U+0029 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 U+0028 POIs U+0029 is proposed based on a collaborative tensor factorization U+0028 CTF U+0029 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 U+02BC 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 U+0028 Partition-based CTF U+0029 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.