E-commerce recommendation method based on dynamic interest group identification and generative adversarial network

Abstract

The present invention relates to the technical field of data analysis and recommendation systems, in particular to an e-commerce recommendation method based on dynamic interest group identification and generated confrontation network, including: inputting the scoring characteristics of user behavior data, using a trained scoring prediction model to make predictions, The score prediction model outputs the predicted value of the score, and generates a recommendation list to recommend items for users according to the predicted value of the score. The invention utilizes the generated confrontation network compensation data to identify the category of the interest group on the compensated data, solves the problems of the user's non-interest preference information and interest generalization, and has important application value for both the user and the merchant.

Description

technical field [0001] The invention relates to the technical field of data analysis and recommendation systems, in particular to an e-commerce recommendation method based on dynamic interest group identification and generation of confrontation networks. Background technique [0002] With the development of information technology and the Internet, people have gradually entered the era of information overload from the era of information scarcity. In this era, both information consumers and information producers have encountered great challenges: as an information consumer, it is very difficult to find the information that interests you from a large amount of information; as an information producer, How to make the information produced by yourself stand out and attract the attention of the majority of users is also a very difficult thing. The recommender system is an important tool to solve this contradiction. The task of the recommender system is to contact users and inform...

AI Technical Summary

Problems solved by technology

Although the user interest group has a clear identification, the identification of the interest group has not been dynamically changed over time, so that the score prediction has no preference relationship, resulting in inaccurate recommendation results.

[0007] 3. Multidimensional complexity of feature space

Considering the addition of user interest group features, how to transform data dimensions and compress data is facing difficulties

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