Behavior derivation of users based on Naive Bayes web page classification
QIN Peng
CAO Tian-jie
Abstract：Aiming at the situation that the accuracy and recall rate of traditional web page classification are not high and the classification efficiency is low, a web page pre-classification algorithm based on Naive Bayes classification was proposed. According to the online activity situation of users, the relevant websites were extracted, the contents and keywords of web pages were analyzed, and the classification was performed with the Naive Bayes algorithm. According to the browse situation of users on various web pages, the behavior characteristics of users were analyzed. The improved web text weight calculation method was adopted, the web site pre-classification mechanism was introduced, and the processing efficiency of data and classification accuracy were improved. The results show that the web site classification algorithm is accurate, can fully explore the interest and preference of users, and can be applied in both the commercial popularization and forensic evidence as the data algorithm for the behavior analysis of users.
Keywords：web page keywordNaive Bayesweb page classificationbehavior characteristicweight calculation methodwebsite pre-classificationbusiness promotionforensic evidence
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 82-87 )