A Personalized Behavior Recommendation Method Based on Federated Learning

Abstract

The invention discloses a personalized behavior recommendation method based on federated learning, comprising the following steps: step 1, collecting user behavior characteristic data, classifying the user behavior characteristic data, and classifying local clients according to the category of the user behavior characteristic data; Standardize the user behavior feature data, and then construct the standardized user behavior feature data into a feature matrix form; Step 2, build a local model on the local client; Step 3, based on the interaction between the server and the client user, in the A dual subjective logic model is added to the federated learning algorithm to quantify the reputation value of the local model, and finally the trained model parameters and their reputation value are uploaded to the server. It is conducive to privacy and security, and the user's privacy is saved in the local client; it can also implement group recommendations based on individual user recommendations.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to a federated learning-based personalized behavior recommendation method. Background technique [0002] In recent years, with the development of Internet technology, society has entered an era of information explosion. Massive information appears at the same time. On the one hand, people's privacy is easily touched and violated. On the other hand, it is necessary to learn people's behavior habits, thus forming Targeted recommendation system. Therefore, federated learning is designed to analyze data without touching it. However, the existing federated learning algorithms are still lacking in the analysis of personalization, and this field is very commercially valuable. [0003] The patent application number is 202121739511.7, which discloses a flexible and combinable liquid crystal screen intelligent display system. It is an information display system with a central network structu...

AI Technical Summary

Problems solved by technology

[0004] Problem 1: The problem of protecting user privacy has not been resolved

However, the existing laws do not allow companies to obtain users’ private information at will, so it is necessary to consider completing personalized recommendations on the basis of protecting users’ privacy. Laws at home and abroad are now prohibiting companies and individuals from illegally using private data

[0005] Problem 2: The existing recommendation methods are mainly for individuals, not for an age group or a group. The efficiency of recommendation is relatively low, and group activities require uniform movements, clothing or clothing for a group of people. Slogans etc.

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