POI Recommendation Algorithm Based on Geographical Information Matrix Decomposition

The location-based social network (LBSN) is expanding day by day, and people are willing to share their location information. Aiming at the problem that the user-point-of-interest matrix is sparse and it is difficult to obtain the user's preference for candidate locations from implicit feedback, which affects the accuracy of point-of-interest recommendation, this paper proposes a matrix factorization point-of-interest recommendation algorithm (GMF) that integrates geographic information. First, this paper reconstructs the user access location preference matrix by using the characteristics of geographic information conforming to kernel density estimation to alleviate the problem of matrix data sparsity; then, in order to enhance the effectiveness of the matrix decomposition model, we improve the objective function of matrix factorization in the form of an implicit feedback term; Finally, it is verified on the two real datasets, and the results show that the performance of the algorithm is better than other POI recommendation algorithms.