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The state-of-the-art studies started paying attention to comprehensively analyze geographical information,comment information and social information, but there is still insufficient information mining. To this end, this paper proposed a joint probabilistic generative model of multi-information fusion. Firstly, the framework learned interest topics of users and POIs through textual information,by exploiting the aggregated hierarchical Dirichlet process model,which could automatically learn the number of topics,to replace latent Dirichlet allocation model. Secondly ,according to the kernel density estimation method , whose bandwidth depended on the check-in distribution , the framework conducted personalized modeling of geographic information. Thirdly,it also took consideration of sequential patterns,which was the impact of the visited location to the non-visited location. Then , it modeled social relevance comprehensively. At last,based on the joint probabilistic generative model,this paper proposed the TGSS-PGM model, exploiting multi-type contextual information and incorporating these factors effectively. Experimental results in real world social net-work show that the proposed model outperforms state-of-the-art recommendation algorithms in terms of precision and rating error. [ABSTRACT FROM AUTHOR]

In text streaming data, documents of text stream into system continuously, and huge amount and quick update velocity of data pose great challenges for discovering and tracking topics in such text streaming. In order to handle those prob-lems, this paper proposed a collective matrix factorization based topic discovering and tracking model. In order to deal with mas-sive data, it partitioned the streaming data into batches, and in order to track the evolution of topics, it assumed the current topic distribution as the linear conversion of the previous one. This paper applied collective matrix factorization to represent the cur-rent data as both current topic distribution and previous topic distribution, and got the current topic distribution via factoring both matrices. During the process of solving the optimization problem, it got the parameter update strategy by the Karush-Kuhn-Tucker conditions, and gave the corresponding solving algorithm. During the experimental comparison with Yahoo dataset, the proposed algorithm can better discover topic distribution in text streaming than related works, and also can track the evolution of topics as time going. [ABSTRACT FROM AUTHOR]

In topic models of the social networks, these are usually some frequent words, such as Barack Obama, and they appear in many different topics. Users usually are interested in these words, so they cannot be removed while analyzing, and this poses a huge challenge for topic analysis. In order to solve this problem, this paper took popularity into consideration while modeling topics. This paper defined the concept of popularity for nodes in the social networks, and proposed an extended topic model containing node popularity. The experiments show that, the proposed model has better prediction ability, and is more effective in prediction accuracy than related works. [ABSTRACT FROM AUTHOR]