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XRank: Learning More fromWeb User Behaviors
Seoul, Korea September 20-September 22
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CIT.2006.198Sixth IEEE International Conference o ...
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Yi Zhang, Peking University, China
Lei Zhang, Peking University, China
Yan Zhang, Peking University, China
Xiaoming Li, Peking University, China
Link analysis has been widely used to evaluate the importance of web pages. PageRank, the most famous link analysis algorithm, offers an effective way to rank the pages. However, the algorithm ignores three facts. First, nowadays the way that users retrieve information is quite different from the previous way when web search engine was not extensively used. Second, inter-site links and intra-site links should not be treated equally. A link from a different site is more important for a page than that within the same site. Third, most users start their browsing from a homepage, which should be given more weight than other pages. In this paper, we propose a novel ranking algorithm called XRank as a solution to these problems. Experimental results on the CWT100g show that our XRank algorithm outperforms other famous ranking algorithms, including PageRank and Two-Layer PageRank, especially on sites recommendation and web spam avoidance.
Citation:
Yi Zhang, Lei Zhang, Yan Zhang, Xiaoming Li, "XRank: Learning More fromWeb User Behaviors," cit, pp.36, Sixth IEEE International Conference on Computer and Information Technology (CIT'06), 2006
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