Abstract: Sequential patterns in human beings movement play an important role in location recommendation of geosocial network services. The existing location recommendation system must access the user's original check-in location data in order to mine its sequence pattern, but this will disclose the user's location privacy. To solve this problem, a location recommendation algorithm based on differential privacy protection called DPLORE is proposed. Firstly, the transfer counting matrix is constructed based on the original data, and noise is added to the decomposed matrix elements by using Laplace mechanism, which makes the algorithm meet the differential privacy protection. Then, on the basis of Multi-Markov Chain model, an adaptive weight Nth-Markov Chain model, which uses the user's order pattern to recommend the location, is proposed. Finally, experiments show that the proposed algorithm framework DPLORE has better recommendation precision and recall rate than the existing algorithms, and DPLORE achieves a good trade-off between high recommendation precision and strict location privacy protection.