Predictive ranking
Abstract
PageRank (PR) is one of the most popular ways to rank web pages. However, as the Web continues to grow in volume, it is becoming more and more difficult to crawl all the available pages. As a result, the page ranks computed by PR are only based on a subset of the whole Web. This produces inaccurate outcome because of the inherent incomplete information (dangling pages) that exist in the calculation. To overcome this incompleteness, we propose a new variant of the PageRank algorithm called, Predictive Ranking (PreR), in which different classes of dangling pages are analyzed individually so that the link structure can be predicted more accurately. We detail our proposed steps. Furthermore, experimental results show that this algorithm achieves encouraging results when compared with previous methods.
Authors: Haixuan Yang, Irwin King, Michael Rung-Tsong Lyu