Analysis And Research For Key Technology On Content-Based Web Text Filtering

With the rapid development of Internet, the information on it increases in exponent. It has so huge contents and so many sorts that it may be the largest information resources in the world. Internet brings a chance for users to discover much more information than ever, but on the other hand, users often feel confused facing such a disorder, huge information space which contains thousands upon thousands network links.Filtering is one method to help users to obtain the information that mostly fits their needs. The function of information filtering is to select relevant information or eliminate irrelevant information from dynamic information flow on the Internet according to certain criteria and some approaches. Information filtering technology can provide timely and individuated information services for users by terms of different information needs of different users. It has become hotspot problem.This thesis commences the basic concept of information filtering, discusses the principle and basic processing procedure of information filtering, summarizes the classification of filtering systems, analyzes several classic information filtering models and introduces the methods about how to evaluate the performance of filtering systems. Then key techniques in content-based Web text filtering are particularly discussed, that is how to segment Chinese words, how to extract suitable features from documents, and how to construct and update user profile, etc. Then, the thesis proposes a design scheme of content-based Web text filtering system model and describes the details of its implementation. Finally, we get the test result. It has very good information filtering performance.