Titlebook: WEBKDD 2001 - Mining Web Log Data Across All Customers Touch Points; Third International Ron Kohavi,Brij M. Masand,Jaideep Srivastava Conf

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A Framework for Efficient and Anonymous Web Usage Mining Based on Client-Side Tracking,ndustry in recent years. Through WUM, we are able to gain a better understanding of both the web and web user access patterns; a knowledge that is crucial for realization of full economic potential of the web. In this chapter, we describe a framework for WUM that particularly satisfies the challengi

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A Customer Purchase Incidence Model Applied to Recommender Services,e reviewed and adapted for web-based information markets. Second, we present the empirical validation of the model based on data collected from the information market of the Virtual University of the Vienna University of Economics and Business Administration from September 1999 to May 2001.

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LOGML: Log Markup Language for Web Usage Mining,WWWPal system. We generate web-log reports in LOGML format for a web site from web log files and the web graph. We further illustrate the usefulness of LOGML in web usage mining; we show the simplicity with which mining algorithms (for extracting increasingly complex frequent patterns) can be specified and implemented efficiently using LOGML.

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