Semantic impact graphs for information valuation
Proceedings of the eighth ACM symposium on Document engineering
Associated pagerank: improved pagerank measured by frequent term sets
VECIMS'09 Proceedings of the 2009 IEEE international conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems
Pagerank algorithm improvement by page relevance measurement
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Applications of user and context-aware recommendations using ontologies
Conference Internationale Francophone sur I'Interaction Homme-Machine
Computing information value from RDF graph properties
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
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PageRank is an algorithm used by several search engines to rank web documents according to their assumed relevance and popularity deduced from theWeb'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.