Aiming at the high sparsity problem of user's check-in data and user privacy in LBSN, a hybrid recommendation model (SoGeoCat) is proposed. Firstly, the user's potential point-of-interest is learnt from the user potential point of interest data model. Secondly, the user's potential point-of-interest is incorporated into a category based matrix factorization model and then optimized. Finally, the proposed recommended strategy is according to the user and feature matrix and the point-of-interest matrix. Based on the Foursquare real dataset, the experimental results show that:(1) compared with several other recommended models, the algorithm fills the user's potential point-of-interest into the matrix, which can effectively alleviate the impact of data sparsity; (2) the algorithm can protect the user's family information; (3) the influence of the category information in the recommendation model can improve the recommendation effect.