Department of Computer Science; Rutgers, The State University of New Jersey
SVM HeaderParse 0.2
New Brunswick, NJ 08903 USA
SVM HeaderParse 0.1
The use of link analysis and page popularity in search engines has grown recently to improve query result rankings. Since the number of such links contributes to the value of the document in such calculations, we wish to recognize and eliminate nepotistic links --- links between pages that are present for reasons other than merit. This paper explores some of the issues surrounding the question of what links to keep, and we report high accuracy in initial experiments to show the potential for using a machine learning tool to automatically recognize such links. Introduction Recently there has been growing interest in the research community in using analysis of link information of the Web (Bharat & Henzinger 1998; Brin & Page 1998; Chakrabarti et al. 1998b; 1998a; Chakrabarti, Dom, & Indyk 1998; Gibson, Kleinberg, & Raghavan 1998a; 1998b; Kleinberg 1998; Page et al. 1998; Chakrabarti et al. 1999a; Chakrabarti, van den Berg, & Dom 1999; Chakrabarti et al. 1999b; Henzinger et al...
user correction - Legacy Corrections
In AAAI-2000 Workshop on Artificial Intelligence for Web Search