Commodity recommendation method based on mobile electronic commerce of big data

Abstract

The invention requests to protect a commodity recommendation method based on mobile electronic commerce of big data. The method comprises the following steps that: 101: carrying out a preprocessing operation on the historical behavior data of a user; 102: according to behavior time, carrying out a data division operation on the historical data of the user; 103: marking the historical behavior dataof the user; 104: carrying out a feature engineering construction operation on the historical data of the user; 105: establishing a plurality of machine learning models, and carrying out a model fusion operation; and 106: through an established model, according to the behavior data of the user, predicting whether the user purchases a certain commodity in one future day or not. By use of the method, the historical data of the user is preprocessed and analyzed to extract features, the plurality of machine learning models are established so as to predict a probability for the user to purchase the certain commodity in one future day, and accuracy for a merchant to recommend commodities to the user is improved.

Description

technical field [0001] The invention belongs to the technical field of machine learning and big data processing, in particular a mobile recommendation algorithm based on multi-model fusion. Background technique [0002] With the rapid development of the Internet, people's lives are increasingly dependent on the Internet, and people's basic necessities of life have been associated with the Internet. Therefore, the e-commerce system that provides online shopping services is playing an increasingly important role in today's society. And has gradually developed into the backbone of our emerging industries, but due to the increasing amount of information, e-commerce systems are facing enormous challenges at the same time. However, the traditional product recommendation algorithm cannot effectively solve the problems of information redundancy, low algorithm efficiency, and low recommendation accuracy. The present invention is based on the traditional recommendation algorithm, a p...

AI Technical Summary

Problems solved by technology

[0002] With the rapid development of the Internet, people's lives are increasingly dependent on the Internet, and people's basic necessities of life have been associated with the Internet. Therefore, the e-commerce system that provides online shopping services is playing an increasingly important role in today's society. And has gradually developed into the backbone of our emerging industries, but due to the increasing amount of information, the e-commerce system is facing huge challenges at the same time

However, the traditional product recommendation algorithm cannot effectively solve the problems of information redundancy, low algorithm efficiency, and low recommendation accuracy.

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