Matrix decomposition interest point recommendation method based on geographic position fusion social influence and category popularity

Abstract

The invention discloses a matrix decomposition interest point recommendation method based on geographic position fusion social influence and category popularity. The method comprises the steps of obtaining historical sign-in information of all users and candidate interest points in the position social network, calculating a geographical correlation coefficient of a target user to obtain social network information of all the users, and calculating a social correlation coefficient S (xu, l) between the target user and the interest points; calculating a category popularity correlation coefficientC (yu, l) between the target user and the interest point based on the preference of the candidate interest point category and the popularity of the candidate interest point; fusing the geographic information model, the social correlation coefficient, the category popularity correlation coefficient and the potential features through a probability matrix decomposition method to form an interest point recommendation model, and calculating a recommendation score Recu, l of the target user; and generating recommendation information based on the recommendation score Recu, l, thereby realizing the purpose of personalized recommendation.

Description

technical field [0001] The present invention relates to a point of interest recommendation method, more specifically, to a matrix decomposition method for point of interest recommendation based on geographical location and fusion of social influence and category popularity. Background technique [0002] With the rapid development of social networks, smart mobile terminals and automatic positioning technology, location-based social network (Location-Based Social Network, LBSN) emerged as the times require, providing people with extremely convenient location services. Typical LBSN applications include Foursquare , Yelp and Gowalla etc. LBSN connects the online virtual society with the offline real world. Users visit the points of interest they are interested in offline. The check-in experience of the point of interest. POI recommendation is an important part of the LBSN service. By counting the historical check-in data of users, it aims to recommend potential POIs for users....

AI Technical Summary

Problems solved by technology

Therefore, the user-POI check-in matrix used in different collaborative filtering methods becomes very sparse, and the extreme sparsity of data will undoubtedly bring great difficulties to POI recommendation, which will lead to low accuracy of recommendation results

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