Position social network-oriented geographic position regularization-based interest point recommendation method

Abstract

The invention discloses a position social network-oriented geographic position regularization-based interest point recommendation method. The method comprises the following steps of: establishing a sign-in frequency matrix of users and interest points and calculating preference confidence coefficients, for the interest points, of the users; calculating similarity degrees between the interest points on the basis of geographic position coordinates between the interest points; establishing an interest point neighborhood relationship matrix, and representing feature vectors of target interest points by feature points of adjacent interest points; generating a target function on the basis of a weight probability matrix decomposition model; generating a final potential feature matrix of the usersand the interest points; generating a predicted scoring matrix according to the final potential feature matrix of the users and the interest points; and carrying out personalized recommendation on the users. According to the method, geographic relationships between interest points are explored from the angle of position, and the geographic relationships are fused into weight probability matrix decomposition models in a regular term manner, so that recommendation targets are optimized, the correctness and recall rate are improved, better recommendation effect is obtained, and the method more accords with position social network characteristics.

Description

technical field [0001] The invention relates to the field of social interest recommendation, in particular to a location-oriented social network-based method for recommending points of interest based on geographic location regularization. Background technique [0002] In recent years, in order to help users find more attractive locations and meet their individual needs for POIs, Point-of-Interest (POI) recommendation, as an important information filtering technology, has been favored by researchers. widespread attention. Compared with traditional recommendation problems, POI recommendation mainly has the following two challenges: 1) In location social network (LBSN), user’s preference is represented by the user’s implicit check-in data, and we can only know that the user has visited in the past Which locations have been visited, but for the locations that have not been visited, we do not know whether the user does not like it or because they have not found it. It is difficu...

AI Technical Summary

Problems solved by technology

For example, Liu et al. used geographical influence to model from the perspective of location. The proposed IRenMF algorithm improved the recommendation performance, but their work did not consider the explicit relationship between the two places.

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