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Since the existing works of POI(point-of-interest) recommendation on location-based social networks(LBSN) focus on mining context information of POI, including the geographical information, comment information and the temporal information, which the comment information of user has not been systematically studied. This paper proposed a unified POI recommendation model, which fused user preference to a POI with temporal information, geographical influence and comment information of user. The model studied the comment information of LBSN by exploiting the latent Diriehlet allocation(LDA) model and modeled the user preference based on the number of user check-in behaviors. Finally, experimental results in real world social network show that the proposed model outperforms state-of-the-art recommendation algorithms in terms of precision and rating error. [ABSTRACT FROM AUTHOR]