An interest point recommendation method based on deep semantic analysis of user sign-in behavior changes

The invention discloses a deep semantic analysis interest point recommendation method based on user sign-in behavior changes. The invention relates to the technical field of user behavior analysis. The method comprises the following steps: firstly, dividing a user sign-in area into a local area and a remote area by using Gaussian kernel density estimation, proposing a user sign-in behavior interest change method according to the position of a user sign-in behavior, constructing an LDSSCS model based on matrix decomposition, and then carrying out deep semantic analysis and research on the sign-in behavior of a user. Based on the position change of the user, the method not only can recommend the most possible interested place for the user and improve the life quality of the user, but also can explore potential customers for merchants, increase commercial benefits, provide support for point-of-interest recommendation under a social network based on the position, and promote further development of mobile applicat