Commodity recommendation method based on mobile electronic commerce of big data

A technology for e-commerce and product recommendation, applied in the fields of big data processing and machine learning, can solve problems such as low algorithm efficiency, low recommendation accuracy, and information redundancy, and achieve the effects of reducing dimensions, improving accuracy, and improving efficiency

Inactive Publication Date: 2018-04-13
CHONGQING UNIV OF POSTS & TELECOMM
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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 deve

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  • Commodity recommendation method based on mobile electronic commerce of big data
  • Commodity recommendation method based on mobile electronic commerce of big data
  • Commodity recommendation method based on mobile electronic commerce of big data

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Experimental program
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Embodiment 1

[0064] refer to figure 1 , figure 1 A flow chart of a product recommendation algorithm based on big data mobile e-commerce is provided for Embodiment 1 of the present invention, specifically including:

[0065] 101. Collect the user's historical consumption data and perform preprocessing operations on the historical data: collect the user's basic information, user historical behavior information, product information and other information. details as follows:

[0066] Collection of user's historical consumption data includes user ID, product ID, user's behavior type of product, product category, user's geographical distance, and behavior time: user's behavior type of product includes browsing, collecting, adding to shopping cart, purchasing, corresponding to fetching The values ​​are 1, 2, 3, and 4 respectively. If the user's geographical distance is unknown, it can be represented by null, and the behavior time is accurate to the hour level. The specific table structure is a...

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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...

Claims

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Application Information

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IPC IPC(8): G06Q30/06G06Q30/02G06K9/62
CPCG06Q30/0202G06Q30/0631G06F18/253
Inventor 王进邵帅孙开伟欧阳卫华周瑞港罗杰邓欣陈乔松
Owner CHONGQING UNIV OF POSTS & TELECOMM
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