A Deep Learning Model Based on Sparse Matrix for Point-of-Interest Recommendation
Jun Zeng, Haoran Tang, Yinghua Li, Xin He · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2019
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.