Proceedings of the 11th international conference on World Wide Web
Using ODP metadata to personalize search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Web page classification: Features and algorithms
ACM Computing Surveys (CSUR)
Research on adaptive classification algorithm based on non-segment and classified-centre-vector
International Journal of Intelligent Information and Database Systems
Research on classification algorithm and its application in cased-based reasoning
International Journal of Computer Applications in Technology
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PageRank is known to be an efficient metric for computing general document importance in the Web. While commonly used as a one-size-fits-all measure, the ability to produce topically biased ranks has not yet been fully explored in detail. In particular, it was still unclear to what granularity of "topic" the computation of biased page ranks makes sense. In this paper we present the results of a thorough quantitative and qualitative analysis of biasing PageRank on Open Directory categories. We show that the MAP quality of Biased PageRank generally increases with the ODP level up to a certain point, thus sustaining the usage of more specialized categories to bias PageRank on, in order to improve topic specific search.