Method for determining Internet surfing behavior categories of network users

Abstract

The invention provides a method for determining Internet surfing behavior categories of network users. The method comprises the steps that Internet surfing behavior features of all the network users to be detected are extracted, and a user behavior feature matrix X is formed through a quantitative method of a document vector space model; according to the user behavior feature matrix X, a behavior tendency set T and a user-tendency probability distribution matrix D are obtained through a probability latent semantic analysis PLSA and an EM algorithm; according to the user behavior feature matrix X, a feature word-category probability distribution matrix C is obtained through a support vector machine (SVM) algorithm; T*C runs through matrix multiplication to obtain a tendency-category mapping matrix M; D*M runs through matrix multiplication to obtain a user-category probability distribution matrix Y; according to the probability distribution situation of any network user to be detected in the categories, the network user to be detected is classified into the category with the maximum probability value.

Description

technical field [0001] The invention relates to the technical field of the Internet, in particular to a method for determining the online behavior category of network users. Background technique [0002] A large number of cases show that the level of content security management can be effectively improved by using user behavior category information. However, based on the behavior data of web users, it is very difficult to accurately predict the class probability distribution of users by relying on existing techniques. On the one hand, the traditional classification method is to label the sample set, but because it is very difficult or even infeasible to label some users, the classifier based on it is often not accurate enough and the prediction effect is not ideal. On the other hand, traditional clustering methods can only divide users into one cluster, and a user may have multiple types of behavior tendencies. Therefore, traditional clustering-based classification methods ...

AI Technical Summary

Problems solved by technology

However, based on the behavior data of network users, it is very difficult to accurately predict the user's category probability distribution by relying on existing techniques.

On the one hand, the traditional classification method is to label the sample set, but because it is very difficult or even infeasible to label some users, the classifier based on it is often not accurate enough and the prediction effect is not ideal

On the other hand, the traditional clustering method can only divide users into one cluster, and a user may have multiple categories of behavior tendencies, so the classification method based on traditional clustering cannot reflect the real category distribution of network users

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