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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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. 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. With the increasing popularity of location-based services, we have accumulated a lot of location data on the Web. In this paper, we are interested in answering two popular location-related queries in our daily life: (1) if we want to do something such as sightseeing or dining in a large city like Beijing, where should we go? (2) If we want to visit a place such as the Bird@?s Nest in Beijing Olympic park, what can we do there? We develop a mobile recommendation system to answer these queries. In our system, we first model the users@? location and activity histories as a user-location-activity rating tensor. Because each user has limited data, the resulting rating tensor is essentially very sparse. This makes our recommendation task difficult. In order to address this data sparsity problem, we propose three algorithms based on collaborative filtering. The first algorithm merges all the users@? data together, and uses a collective matrix factorization model to provide general recommendation (, 2010 [3]). The second algorithm treats each user differently and uses a collective tensor and matrix factorization model to provide personalized recommendation (, 2010 [4]). The third algorithm is a new algorithm which further improves our previous two algorithms by using a ranking-based collective tensor and matrix factorization model. Instead of trying to predict the missing entry values as accurately as possible, it focuses on directly optimizing the ranking loss w.r.t. user preferences on the locations and activities. Therefore, it is more consistent with our ultimate goal of ranking locations activities for recommendations. For these three algorithms, we also exploit some additional information, such as user-user similarities, location features, activity-activity correlations and user-location preferences, to help the CF tasks. We extensively evaluate our algorithms using a real-world GPS dataset collected by 119 users over 2.5 years. We show that all our three algorithms can consistently outperform the competing baselines, and our newly proposed third algorithm can also outperform our other two previous algorithms. Point-of-Interest (POI) recommendation has become an important means to help people discover attractive locations. However, extreme sparsity of user-POI matrices creates a severe challenge. To cope with this challenge, viewing mobility records on location-based social networks (LBSNs) as implicit feedback for POI recommendation, we first propose to exploit weighted matrix factorization for this task since it usually serves collaborative filtering with implicit feedback better. Besides, researchers have recently discovered a spatial clustering phenomenon in human mobility behavior on the LBSNs, i.e., individual visiting locations tend to cluster together, and also demonstrated its effectiveness in POI recommendation, thus we incorporate it into the factorization model. Particularly, we augment users' and POIs' latent factors in the factorization model with activity area vectors of users and influence area vectors of POIs, respectively. Based on such an augmented model, we not only capture the spatial clustering phenomenon in terms of two-dimensional kernel density estimation, but we also explain why the introduction of such a phenomenon into matrix factorization helps to deal with the challenge from matrix sparsity. We then evaluate the proposed algorithm on a large-scale LBSN dataset. The results indicate that weighted matrix factorization is superior to other forms of factorization models and that incorporating the spatial clustering phenomenon into matrix factorization improves recommendation performance.