A Framework of Feedback Search Engine Motivated by Content Relevance Mining
Yuexian Hou, Honglei Zhu, Pilian He · 2006
Most current Web search engines generate search results by analyzing queries and relevance between queries and Web-pages. However, as the number of Web-pages grows, this approach appears to be less efficient in finding relevant information. In many situations, search engines cannot determine what kind of information users want. We propose a framework of feedback search engine (FSE), which not only analyzes the relevance between queries and Web-pages but also uses clickthrough data to evaluate page-to-page relevance and re-generate content relevant search results. The efficient algorithms facilitating the framework are described. Making use of dynamical re-generating search results, FSE can provide its users more accurate and personalized information