Web user access path prediction method based on recurrent neural network

Abstract

The present invention relates to a Web user access path prediction method based on a recurrent neural network. In the method provided by the present invention, a user access path is taken as a research target, a recurrent neural network is introduced into a path prediction problem, and a network model for path prediction is studied and designed; based on the simple recurrent neural network, a feature layer is added, and a Long-Short Term Memory (LSTM) unit is used in a hidden layer; and the method can effectively utilize the context information of the user session sequence, learn and memorizethe access rules of the user, obtain good model parameters through training data learning, and then predict the next access path of the user. The theoretical analysis and experimental results show that the method disclosed by the present invention is relatively high in path prediction efficiency, relatively accurate in prediction result, and applicable to solving the problem of Web user access path prediction.

Description

technical field [0001] The invention belongs to the field of Web log mining, and specifically proposes a Web user access path prediction method based on a cyclic neural network for the user access path prediction problem. Background technique [0002] With the prosperity of the Internet, websites have become an important platform for information sharing, but a large number of Web pages make it difficult for users to quickly locate the information they need. With the development of the Internet, the Web has become one of the important platforms for Internet users to obtain information. Users can access the Internet through personal computers, mobile devices, etc., and obtain interesting information at any time, but how to quickly locate the required information in the massive data has become a difficult problem. Mainly manifested in the following aspects: [0003] (1) Site information redundancy and information explosion affect the efficiency of obtaining the required infor...

AI Technical Summary

Problems solved by technology

In addition, a matrix can be used to describe user sessions, which avoids the dimension disaster and data sparsity problems that may be caused by using the frequency of URL occurrences to represent sessions, and can also improve the use of multiple groups similar to to express access sequences. Problems that are not easy to perform complex calculations

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