Interest point recommendation method based on user dynamic preference and attention mechanism

Abstract

The embodiment of the invention provides an interest point recommendation method on user dynamic preference and an attention mechanism. The interest point recommendation method comprises the followingsteps: S1, obtaining a historical behavior record of a user, constructing a historical behavior sequence of the user, and dividing the historical behavior sequence of the user into a long-term historical behavior sequence and a short-term historical behavior sequence; S2, respectively inputting the long-term historical behavior sequence and the short-term historical behavior sequence into a long-term preference model and a short-term preference model to learn the long-term preference and the short-term preference of the user; S3, integrating the long-term preferences and the short-term preferences of the users to obtain final preferences of the users; and S4, calculating a score of the user to the place according to the final preference of the user, and recommending interest points to theuser according to the score of the user to the place. According to the interest point recommendation method, dynamic modeling of user preferences is realized, and accurate representation of the userpreferences can be obtained, and the interest point recommendation effect is improved.

Description

technical field [0001] The present invention relates to the technical field of sequence recommendation, in particular to a point-of-interest recommendation method based on user dynamic preference and attention mechanism. Background technique [0002] With the rapid development of mobile smart devices, positioning technology and mobile Internet technology, location-based social network (Location-Based Social Network, LBSN) began to appear and become more and more popular. Users can share their current location and location-related content information, such as text or images, by checking in in a social network. The location shared by the user is also called a point of interest (POI). Using the rich historical records of users in LBSN to mine users' behavior habits and interest preferences, and recommending places that users may be interested in is called point-of-interest recommendation. POI recommendation plays a very important role in LBSN. It can not only meet the individu...

AI Technical Summary

Problems solved by technology

[0005] Although the above POI recommendation methods have achieved good results, there are still several problems: First, users’ interests are complex and diverse, and most studies treat the locations visited by users as the same, without distinguishing between different locations. The importance of user interest, so a lot of noise is introduced in the process of user preference modeling

Second, users' interests change dynamically over time. Most methods only model long-term stable preferences of users, ignoring users' short-term interests and needs, so that they cannot accurately capture users' interests and preferences.

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