Research And Improvement On Model Of Personalized Search Engine

Through search engine, people could easily get the content what they need. Compared with the old one, the search engine today has a large development. But, there are also some problems, for example, the search engine is not intelligent enough, they can not get the really interested answers of the users from amount of searching results. And it is just the attitude of the research.In allusion to the need of search engine's personalized service, the author puts forward the ranking pages algorithm of personalized search engine: The thesis judged whether the user was interested in the web through the user's action based on the web data-mining method. The author cheese clustering to class the webpage, based on the analysis for original search engine technology. The thesis build on a Key-word and User-interest table for the User-interest message's storing, and build on a web-type table to support the User-interest table. Through analyzed the works, the author give a rank formula which could get the proper result through the User-interest message storing in the User-interest table. At the same time, this thesis build up a model of personalized search engine and the realization of each part of the system are analyzed and designed. The purpose for adding personalized and page-type analyzing model is to improve the personalized analyzing ability, to make the searching results conform users' need and improve users' satisfaction for the personalized search engine.At last, the author has confirmed the better users' satisfaction of the model by experiments compared with the traditional search engine. Also, the author brings forward the direction of the next step of research and some potential problems.