An intelligent surfer model combining web contents and links based on simultaneous multiple-term query

The PageRank algorithm, proposed by [Page et al., 1998] is used in the Google search engine to improve the results of requests by taking into account the link structure of the Web. PageRank give the same weight to all pages that is the surfer model is proposed using a uniform distribution. Richardson and Domingoshave proposed a more interesting and intelligent surfer model combining the link and content information in PageRank. Given a multiple term query, the surfer selects a term according to some probability distribution and uses that term to guide its behavior. We propose to improve this algorithm by using a simultaneous multiple-terms query model. Firstly we propose a measure of relevance of a page to a simultaneous multiple terms query. Then we develop our performed intelligent surfer model. To evaluate the performance, we have tested our algorithm on the "Moroccan ministry tourism\´s Web and show that the performance is superior to that obtained by the existing algorithms.