Modeling method and device for user behavior analysis and prediction based on BP neural network

Abstract

The invention relates to the field of computer networks, especially the field of e-commerce and big data analysis, and more particularly relates to a modeling method and a modeling device for user behavior analysis and prediction based on a BP neural network. The modeling method applies user behavior data acquisition and analysis as well as a budgeting algorithm to parameter source modeling, adopts a three-layer neural network model for design, constructs a neural network prediction model under the three-layer neural network model for predicting mobile user behaviors, and regards behavior types of users as evaluation parameters. The modeling method comprises the steps of: regarding indexes of network user behaviors as parameters, inputting the parameters into the BP neural network in one-to-one correspondence; and subjecting all the input parameters to continuous iterative processing in a hidden layer, and outputting a result through an output layer. The modeling method is characterized by utilizing an artificial bee colony ABC algorithm to compensate for the shortcomings of the BP neural network prediction model, and applying the artificial bee colony ABC algorithm to the operation of the hidden layer and the output layer, thereby improving the convergence rate of the prediction model.

Description

technical field [0001] The present invention relates to the field of computer network, especially the field of e-commerce and big data analysis, in particular to a modeling method and device for user behavior analysis and prediction based on BP neural network. Background technique [0002] In recent years, the national strategy for the rapid development of the Internet has made the use of the Internet more in-depth into the corners of social life and production. Analyzing and predicting people's behavior on the Internet has become the core competitiveness of many commercial companies. By collecting the user's online clicks, consumption, collection, shopping and other behaviors, it is possible to understand his personality characteristics, purchase preferences, and current consumption needs, so as to accurately realize product marketing and promotion, and push different advertisements for different groups of people. , to reduce people's blind consumption. At the same time, ...

AI Technical Summary

Problems solved by technology

However, it needs to improve its slow convergence speed and inaccurate prediction.

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