軟件工程碩士鏈接分析技術(shù)和網(wǎng)頁排名欺詐人民大學(xué)學(xué)術(shù)報(bào)告會(huì)

來源：在職研招網(wǎng)（www.veritoman.com）【在職研究生網(wǎng)】發(fā)布時(shí)間：2010/4/8

軟件工程碩士鏈接分析技術(shù)和網(wǎng)頁排名欺詐人民大學(xué)學(xué)術(shù)報(bào)告會(huì)
學(xué)術(shù)報(bào)告:鏈接分析技術(shù)和網(wǎng)頁排名欺詐
演講人: 微軟亞洲研究院研究員高斌博士
講座時(shí)間: 2010年4月6日（周二）晚6：00~7：30
講座地點(diǎn): 中國人民大學(xué)信息學(xué)院四層報(bào)告廳
講座內(nèi)容:
演講人：微軟亞洲研究院研究員高斌博士
演講人履歷：
Dr. Bin Gao is an associate researcher at Microsoft Research Asia. His current research interests include large-scale graph mining, graph ranking, information retrieval, machine learning, and search log mining. So far, Dr. Gao has more than 15 quality papers published in referred international conferences and journals, including SIGIR, WWW, KDD, ACM MM, TKDE, and IRJ. He has over 10 filed US / international patents or pending applications. He is the winner of the Best Student Paper Award in SIGIR 2008. He has been the program committee members for international conferences like SIGIR, KDD, ICDM, etc., and serves as reviewer for international journals like TKDE, TMM, PRL, IRJ, etc. Prior to joining Microsoft, he got his PhD degree from School of Mathematical Sciences, Peking University in 2006, where his research efforts were on pattern recognition and machine learning. Before studying in Peking University, he got his BS degree from School of Mathematical and System Sciences, Shandong University in 2001.
演講摘要：
The Web contains billions of interconnected web pages. It can thus form a large-scale graph with web pages as nodes and links as edges. Link analysis on the large-scale graph is useful for many Web applications like page importance computation and index selection.
Spam has become one of the biggest challenges for web search engines. Many anti-spam technologies have been developed to automatically detect web spam and eliminate related spam sites from search results. These technologies can accurately identify spam by analyzing the content and topological structure of the Web, utilizing temporal information of the web, and mining from behavior data of Web users.

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