A Deep Learning Model Based on Sparse Matrix for Point-of-Interest Recommendation

Point-of-interest (POI) recommendation that consists of location-based social networks (LBSNs) and provides personal services for users has become an important part in the field of recommendation system. Due to the sparseness of user check-in matrix, POI recommendation faces great challenges. However, most researches just consider of spatial and temporal impact on recommendation and do not solve the problem of sparsity. This paper proposes a POI recommendation model called RBMNMF which is based on sparse matrix of user check-ins. Firstly, by stacking restricted Boltzmann machines (RBM), the potential relationship between users and POIs is learned and multiple user-POI matrices are extracted. Second, fill the original sparse matrix by using non-negative matrix factorization (NMF). Finally, fuse those prediction matrices to generate final POI recommendation for users, which is benefit for solving the problem of sparsity effectively. Experiments on real-world data set prove that the model we propose has a better accuracy than traditional algorithms.