Point-of-Interest Recommendation System based on DeepWalk and Tensor Decomposition

With the popularization of mobile devices and the development of satellite positioning technology, it is more convenient to collect the travel data of users. A large amount of user sign-in data provides strong data support for points of interest recommendation. The rapid development of transportation and the improvement of people’s material living standards have made point-of-interest recommendations an urgent need. Because the user’s travel is a continuous behavior with a trajectory. We can dig out important information from the adjacent sign-in records. For example, a large number of users will go to place B after they have been to place A, which shows that the connection between place A and place B is very close. In this paper, the graph embedding method is used to mine users’ favorite degree of places, and the tensor decomposition method is used to mine users’ travel interests, so as to analyze the relationship between users’ travel characteristics and places. Based on the idea of collaborative filtering, we need to find similar user groups in the dataset. In this paper, a method for calculating weighted virtual users is proposed, and similar users are clustered by using Gaussian mixture clustering method to get similar user groups. Experimental results show that the method implemented in this paper has better effect than other methods.