A search result ranking algorithm based on web pages and tags clustering

With the rapid development of network techniques, huge information resources glut the whole web world. Locating useful information effectively from the World Wide Web (WWW) is of wide interest. The huge volume of the return results makes the user only focus on the top results. So the ranking problem becomes the important task for the search systems. Now, so many ranking algorithms were proposed. But the existing search systems just use the relationships or links between user query and web pages in the traditional Web1.0 environment. Now, the success and popularity of social network systems, such as del.icio.us, Facebook, etc., have generated many interesting problems to the research community. This gives us a new viewpoint on how to improve the quality of information retrieval. This paper firstly summarizes the present ranking algorithms, and analyses their merits and demerits. Secondly, we present a new search ranking algorithm bases on web pages and tags clustering, and use several evaluating methods to assess and contrast with Google.