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

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...

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 amount of latest data; second, the existing methods are not suitable for large In data scenarios, it takes a long time to train the model; third, the existing methods cannot effectively protect the privacy of users’ personal information

Images