PageRank is primarily based on link structure analysis. Recently, it has been shown that content information can be utilized to improve link analysis. We propose a novel algorithm that harnesses the information contained in the history of a surfer to determine his topic of interest when he is on a given page. As the history is unavailable until query time, we guess it probabilistically so that the operations can be performed offline. This leads to a better web page categorization and, thereby, to a better ranking of web pages. 1