Framework of Feedback Search Engine Based on Content Relevance Mining
Ruiguo Yu · 2008
Current mainstream search engines generate search results by analyzing statistical information such as the frequency of queries in web pages and the ranking of web pages.But search engines cannot determine what kind of information users want because queries are often simple in many situations.A web content relevance mining method was put forward which uses large amounts of clickthrough data.Furthermore,based on this method,a framework of feedback search engine(FSE)and associated algorithms were proposed.According to page-to-page relevance,FSE generated search results dynamically to provide users with more accurate and personalized information.Based on real clickthrough data,experiments on the compressibility and effectiveness of the web relevance matrix were performed.And the experimental results demonstrate the feasibility of the proposed framework.