Knowledge learning and privacy protection based big-data user purchase intention predicating method

A privacy protection and knowledge learning technology, applied in the field of marketing, can solve the problems of long training model, low accuracy, and inability to effectively protect the privacy of users' personal information.

Active Publication Date: 2015-02-04
常州化龙网络科技股份有限公司
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0005] The technical problems to be solved by the present invention are: first, the existing methods for predicting user purchase intentions are not accurate enough in the context of a large amount of historical related data and a small

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  • Knowledge learning and privacy protection based big-data user purchase intention predicating method
  • Knowledge learning and privacy protection based big-data user purchase intention predicating method
  • Knowledge learning and privacy protection based big-data user purchase intention predicating method

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Embodiment Construction

[0043] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific examples and with reference to the accompanying drawings.

[0044]This embodiment selects the survey data of a certain digital product as the research object, wherein the historical data is the survey data of other models of digital products of this brand half a year ago, including 10,000 samples in total, and the current data is the survey of new digital products of the same brand launched recently The data contains a total of 100 samples. All data includes a total of 40 attributes, including: AGE, SEX, MARITAL, JOB, TRAVTIME, STATECOD, DOMESTIC, BRAND, MODEL NUMBER, AMOUNT, PRICE, RETURN, etc.

[0045] Step 1: Normalize a large number of historical data samples and a small number of current data samples;

[0046] In order to improve the accuracy of the prediction method, the data ...

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Abstract

The invention discloses a knowledge learning and privacy protection based big-data user purchase intention predicating method which comprises following steps of: (1) performing normalization processing on a large number of historical data and a small number of current data; (2) grouping the data and establishing a training sample set; (3) counting user purchase intention probability of each group; (4) calculating group labels; (5) training the training set by using an improved support vector machine; (6) constructing a prediction function; (7) inputting to-be-predicted data into the predication function to obtain a prediction result. As the improved support vector machine is used in the method, the small number of current data set probability information and the large number of historical data set probability information are blended into a structural risk minimization learning framework, learning of knowledge in different periods is realized by virtue of constructing similar distance items among data, and accordingly, the knowledge learning and privacy protection based big-data user purchase intention predicating method which is applicable for learning problems of big samples is constructed.

Description

technical field [0001] The invention belongs to the technical field of marketing, relates to pattern recognition technology, and is a method for predicting purchase intentions of big data users based on knowledge learning and privacy protection. Background technique [0002] The invention belongs to the technical field of marketing, relates to pattern recognition technology, and is a method for predicting purchase intentions of big data users based on knowledge learning and privacy protection. [0003] Consumers are the guides of various business activities of enterprises, and consumers' purchase intention is the basis of purchase behavior, which can be used to predict consumer behavior. From the perspective of marketing, when the enterprise has mastered the consumers' willingness to purchase, it can reasonably arrange the purchase of raw materials, adjust the structure of the product, and formulate the production plan of the product; Recommend relevant products to consumer...

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Application Information

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IPC IPC(8): G06Q30/02G06F17/30
Inventor 倪彤光顾晓清孙霓刚林逸峰
Owner 常州化龙网络科技股份有限公司
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