Next interest point recommendation method based on user relationship embedding model

Abstract

The invention discloses a method for recommending a next interest point based on a user relationship embedding model, which comprises the following steps of: respectively establishing a friend relationship table and a preference similarity relationship table according to friend relationships and user historical sign-in record data, and establishing a user relationship graph through the two tables;obtaining a user relationship sequence by adopting a random walk algorithm according to the established user relationship diagram, and obtaining a low-latitude embedding vector of each user by the user relationship sequence through a Word2Vec word embedding model; and initializing user embedding layer parameters of the neural network by using the low-latitude embedding vector of the user, and giving a next interest point recommendation by using a gating cycle unit. According to the next interest point recommendation method based on the user relationship embedding model, the preference similarity relationship is introduced into the recommendation model, the representation of the user relationship is enhanced, the limitation problem of the recommendation model caused by only considering theone-sidedness of the friend relationship of the user in the existing method is solved, and the accuracy of the recommendation model is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of neural networks and recommendation systems, and in particular relates to a method for recommending the next point of interest based on a user relationship embedding model. Background technique [0002] With the widespread application of location-based social networking software (such as gowalla, foursquare, etc.), a large amount of check-in information has been collected, and these rich check-in data are used to recommend the next point of interest (such as scenic spots, hotels, restaurants, etc.) that users are interested in. etc.) will enhance the user's service experience and user loyalty, and will also increase huge profits for the enterprise. Existing recommendation methods only consider users' social relationships (i.e., friend relationships), and these studies are based on the assumption that "in social networks, friends often have the same or similar tastes". However, friend relations cannot expr...

AI Technical Summary

Problems solved by technology

[0003] In view of the fact that the existing technology cannot effectively model the user relationship, ignoring the preference similarity relationship, the technical problem solved by the present invention is to provide a method for recommending the next POI based on the user relationship embedding model, which can effectively model the user relationship Carry out modeling, consider complex user relationships, and use the present invention to integrate user relationships to effectively improve the accuracy of the recommendation system

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