Personalized versions of PageRank have been proposed to rank the results of a search engine based on a user’s topic or query of interest. This paper introduces a methodology for personalizing PageRank vectors based on URL features such as Internet domains. Users specify interest profiles as binary feature vectors where a feature corresponds to a DNS tree node. Given a profile vector, a weighted PageRank can be computed assigning a weight to each URL based on the match between the URL and the profile features. We present promising preliminary results from a small experiment in which users were allowed to select among nine URL features combining the top two levels of the DNS tree, leading to 2 9 pre-computed PageRank vectors from a Yahoo crawl. Personalized PageRank performed favorably compared to pure similarity based ranking and traditional PageRank
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