Usage Aware PageRank

Abstract

Traditional link analysis approaches assume equal weights assigned to different links and pages. In original PageRank formulation, the user model assumes that the user has equal probability to follow each link from a given page, thus the score of a page equally affects all of the pages it points to. It also assumes that the probability for a user to go to a URL directly without following a link is the same for all URLs. In this paper, we investigate different weighting schemes that take into account the probability to go to a page directly (by typing or using bookmarks), as well as the relative probability to follow a link from a given page. Both of these probabilities can be approximated from usage logs if they are available. We introduce a naturalextension to the original PageRank formulation that we will call Usage aware PageRank (UPR). The new formulation combines static link structure graph with the usage graph that will be obtained via web logs or other means. It is also quite general; how much emphasis will be given to the graphs is controlled by a parameter. If the parameter is set to zero, the algorithm becomes equivalent to the original PageRank, if it is set to one, the emphasis shifts to the usage graph, and for values in between, both of the graphs will be used with weights specified by the parameter. UPR is also quite inexpensive. After a onetime precalculation step, an iteration of UPR takes about the same time as a PageRank iteration.