Web Intelligence, IEEE / WIC / ACM International Conference on (2005)
Compi?gne University of Technology, France
Sept. 19, 2005 to Sept. 22, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WI.2005.161
Tie-Yan Liu , Microsoft Research Asia
Wei-Ying Ma , Microsoft Research Asia
In this paper, we propose a novel method to calculate the webpage importance based on a conditional Markov random walk model. The main assumption in this model is that given the hyperlinks in a webpage, users are not really randomly clicking one of them. Instead, many factors may bias their behaviors, for example, the anchor text, the content relevance and the previous experiences when visiting the website that a destination pages belongs to. As one of the results, the user might tend to visit those pages in high-quality websites with higher probability. To implement this idea, we reformulate the Web graph to be a two-layer structure, and the webpage importance is calculated by conditional random walk in this new Web graph. Experiments on the topic distillation task of TREC 2003 Web track showed that our new method can achieve about 18% improvement on mean average precision (MAP) and 16% on precision at 10 (P@10) over the PageRank algorithm.
W. Ma and T. Liu, "Webpage Importance Analysis Using Conditional Markov Random Walk," Proceedings. The 2005 IEEE/WIC/ACM International Conference on Web Intelligence(WI), Compiegne, France, 2005, pp. 515-521.