The Research Of Personalized Search Engine Based On Analysis Of Click Data

With the rapid expansion of information technology throughout the world, Internet has become the main platform of information releasing, exchanging and acquiring. While enjoying the convenience and abundant information bringing by the Internet, people also encounter the problem inevitably that they cannot get efficient information rapidly. As a handy entry for people to gain information, Search engine is used widely and depended on by people.But, the traditional search engines offer only one uniform entrance for all network users, and always return a same result list if given a same query although may queried by different person. The result list contains a lot of information remain, and the information the user interested in may submerged by many redundant things. To understand user's query motivation deeply, and provide personalized service for different people, technologies of personalized search are put forward and researched.However, research work of personalized search is still in a state that good and evil ones mixed up. And there is no commercial personalized search engine which gives a personalized service that can let us feel new and fresh. Herein the status quo and problem of the personalized search, this thesis proposed a personalized search scheme based on analysis of click data. The main contents are as follows.(1) Gave an analysis on related technologies of personalized searching, and then put forward the weakness and problems of the personalized search engine nowadays.(2) Proposed an integrated strategy which extracts implicit relevance feedback by analyzing users'click data. It has much more value in actual application than explicit feedback.(3) Brought forward a personalized PageRank algorithm based on adding amendatory vector, and put the implicit relevance feedback which was extracted from click data into the algorithm, then implemented a personalized ranking method of searching result.(4) Used the collaborative filtering into personalized PageRank algorithm, and improved the quality of the searching result ranking by using relevance feedback of others'in the group who owns similar interests.(5) Proposed a method of classifying users based on clustering basal users'interesting, so as to implement the reasonable grouping of users, and decrease the complexity of the system.