Experimental Bounds on the Usefulness of Personalized and Topic-Sensitive PageRank

PageRank is an algorithm used by several search engines to rank web documents according to their assumed relevance and popularity deduced from the Web's link structure. PageRank determines a global ordering of candidate search results according to each page's popularity as determined by the number and importance of pages linking to these results. Personalized and topic-sensitive PageRank are variants of the algorithm that return a local ranking based on each user's preferences as biased by a set of pages they trust or topics they prefer. In this paper we compare personalized and topic-sensitive local PageRanks to the global PageRank showing experimentally how similar or dissimilar results of personalization can be to the original global rank results and to other personalizations. Our approach is to examine a snapshot of the Web and determine how advantageous personalization can be in the best and worst cases and how it performs at various values of the damping factor in the PageRank formula.