Recommendation method based on graph interaction network

Abstract

A recommendation method based on the graph interaction network is applied to the field of user personalized recommendation. A traditional recommendation method and a traditional deep learning method are difficult to meet complex application environments due to rapid development of the Internet industry and continuous increase of the network data volume, and have defects in the aspects of accuracyand space complexity. Therefore, the invention provides the recommendation method based on the graph interaction network, by adopting the method, the personalized recommendation accuracy can be ensured, the space complexity of the model is reduced, and the method has a wide application prospect.

Description

technical field [0001] The present invention is applied to the field of recommendation system based on U-I relationship, and specifically relates to data mining and deep learning technologies such as graph depth network, attention mechanism, user preference information and item attribute information feature extraction, U-I interactive information modeling, etc. Background technique [0002] Personalized recommendation is a comprehensive analysis task, widely used in social network, music radio station, e-commerce, personalized advertisement, movie and video website, etc., so it has attracted much attention. With the rapid development of the Internet industry and the continuous growth of network data volume, recommendation systems are faced with increasingly complex recommendation tasks and application environments. Especially since entering the Web 2.0 era, with the sudden emergence of social network media, Internet citizens are not only consumers of network information, but...

AI Technical Summary

Problems solved by technology

However, traditional deep learning techniques cannot naturally combine user and item information

Generally, based on the interaction between users and items, the deep network is used to extract better user and item features, and then other models or another deep network are used to predict the user's preference for recommended items. Although this multi-model stacking method improves The accuracy of personalized recommendation is improved, but the model is difficult to be deployed conveniently, and the scalability and space complexity of the model are difficult to be effectively guaranteed

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