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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]