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.
Some very recent works have incorporated group behaviors into recommender systems for enhancing performance. T. Yuan proposed a Group-Sparse Matrix Factorization (GSMF) approach to factorize the rating matrices for multiple behaviors into a user and item latent factor space @cite_16 . H. Wang proposed a group-based algorithm for POI recommendation @cite_32 by grouping users of similar interests based on their frequently visited locations' category hierarchy. Chen proposed a novel two-step approach for personalized successive POI recommendation: First, group-based category recommendation by designing a group-based tensor model to predict the location category preference; then, category-based location recommendation by proposing a distance weighted-HITS algorithm to rank the locations under a selected location category @cite_14 .
@cite_14 @cite_16 @cite_32
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The prevalence of smartphones and mobile social networks allow the users to share their location-based life experience much easier. The large amount of data generated in related location-based social networks provides informative cues on user's behaviors and preferences to support personalized location-based services, like point-of-interest (POI) recommendation. Yet achieving accurate personalized POI recommendation is challenging as the data available for each user is highly sparse. In addition, the computational complexity is high due to the large number of users. In this paper, a novel methodology for personalized successive POI recommendation is proposed. First, the preferred successive category of location is predicted using a third-rank tensor computed based on the partially observed transitions between the categories of user's successive locations where the missing transitions are uncovered by inferring the group preference. The group is achieved according to users' demographics and frequently visited locations. Then, a bipartite graph is constructed based on the recommended categories for each user. To obtain the personalized ranking of locations, a distance weighted HITS algorithm is proposed so that the location authority score is updated iteratively according to the visiting frequency of the group and some distance constraints. The proposed two-step approach with the category prediction incorporated aims to boost the location prediction performance via the smoothing and at the same time reduce the complexity. Experimental results obtained based on the real-world location-based social network data show that the proposed approach outperforms the existing state-of-the-art methods by a large margin. Recently, some recommendation methods try to improve the prediction results by integrating information from user's multiple types of behaviors. How to model the dependence and independence between different behaviors is critical for them. In this paper, we propose a novel recommendation model, the Group-Sparse Matrix Factorization (GSMF), which factorizes the rating matrices for multiple behaviors into the user and item latent factor space with group sparsity regularization. It can (1) select out the different subsets of latent factors for different behaviors, addressing that users' decisions on different behaviors are determined by different sets of factors; (2) model the dependence and independence between behaviors by learning the shared and private factors for multiple behaviors automatically; (3) allow the shared factors between different behaviors to be different, instead of all the behaviors sharing the same set of factors. Experiments on the real-world dataset demonstrate that our model can integrate users' multiple types of behaviors into recommendation better, compared with other state-of-the-arts.