  In this paper, we address the problem of personalized next Point-of-interest
(POI) recommendation which has become an important and very challenging task
for location-based social networks (LBSNs), but not well studied yet. With the
conjecture that, under different contextual scenarios, human exhibits distinct
mobility pattern, we attempt here to jointly model the next POI recommendation
under the influence of user's latent behavior pattern. We propose to adopt a
third-rank tensor to model the successive check-in behaviors. By integrating
categorical influence into mobility patterns and aggregating user's spatial
preference on a POI, the proposed model deal with the next new POI
recommendation problem by nature. By incorporating softmax function to fuse the
personalized Markov chain with latent pattern, we furnish a Bayesian
Personalized Ranking (BPR) approach and derive the optimization criterion
accordingly. Expectation Maximization (EM) is then used to estimate the model
parameters. We further develop a personalized model by taking into account
personalized mobility patterns under the contextual scenario to improve the
recommendation performance. Extensive experiments on two large-scale LBSNs
datasets demonstrate the significant improvements of our model over several
state-of-the-art methods.
