An Intelligent Surfer Model Based on Combining Web Contents and Links

The PageRank algorithm is an iterative algorithm used in the Google search engine to improve the results of requests by taking into account the link structure of the web. More interesting and intelligent surfer model combining the link and content information in PageRank have been proposed in the literature. The main disadvantage of those models is that the combination of single word PageRank to calculate the PageRank for a multiple word query is a worse approximation when the words are dependent. 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 SQD-PageRank algorithm on the Amazon's site Web and show that the performance is superior to that obtained by the existing algorithms.