Personalized web page filtering method

Abstract

The invention relates to a web page filtering method which can be individually customized, which comprises steps: extracting the characteristics of a user example web page and a training web page base, excavating the attribute of a user interest class based on semi-supervised learning, conducting the characteristic extraction of the user interest class and the characteristic selection, and filtering the personalized web pages based on a Bayesian classifier. The invention provides a novel web page filtering frame driven by examples, the filtering demands of the users can be expressed through web page examples, the user examples can be any type web pages or multi-type composite web pages, and a web page filter in line with the individual demands of the user can be constructed by means of the semi-supervised learning, thereby overcoming the disadvantages of the limitation to single filtering or limited type web page, and unavailable realization of individual customization in a traditional web page filtering method. The method has the advantages of high accuracy, robustness and operation speed, and has excellent application prospects.

Description

Technical field [0001] The invention relates to the field of computer network technology, in particular to web page filtering technology. Background technique [0002] With the rapid development of the Internet (the Internet), it has gradually become an important part of people's lives. People are becoming more and more dependent on the Internet, and the demand for web filtering is also increasing. On the one hand, due to the openness of the Internet, some bad information is also spread on the Internet, such as pornography, drugs, violence, etc. Such bad information has a great impact on the physical and mental health of people, especially young people, and endangers social stability. On the other hand, due to the explosion of information and the rapid development of the Internet, the amount of information on the Internet is increasing geometrically, but for certain Internet users, most of the information is useless or even spam. Therefore, how to keep what you are interested i...

AI Technical Summary

Problems solved by technology

Due to the large scale of the training database, it is necessary to calculate the similarity between the target webpage and all the webpages in the library when filtering each webpage, and its time complexity may not meet the needs of real-time filtering

In addition, the filtering threshold in this method is domain-dependent, so it needs to be carefully tuned according to the specific filtering test results when implementing personalization, which is another limitation of the practical application of this method.

[0007] Although after a lot of research, web filtering technology still can not meet the actual needs

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