Please use this identifier to cite or link to this item: https://bspace.buid.ac.ae1234/49
Title: Predicting User Behavior over the Web
Authors: Al Safadi, Amal Adnan
Keywords: web usage mining
E-commerce
ECML/PKDD
Issue Date: Jan-2010
Publisher: The British University in Dubai (BUiD)
Abstract: Web Usage Mining is the application of discovering useful patterns from web data using statistical and data mining techniques. It has recently a wide range of applications in E-commerce web site and E-services such as building interactive marketing strategies, Web recommendation andWeb personalization. Due to its importance, the ECML/PKDD conference announced a competition (challenge) where researchers analyze a web-usage data set and attempt to make predictions about user behavior.The purpose of this thesis is to analyze the first problem of ECML/PKDD 2007 challenge and apply web usage mining techniques in order to predict the user navigation behavior, such as the user visit duration and type of visited pages, based on user real historical data. Toward this goal,I applied web usage mining, data preprocessing, and visualization techniques. I also applied different classification algorithms and studied the effect of attribute selection on each classifier performance. The results I report are comparable to the challenge winner and outperform the runner-up on two out of the three challenge problems.
Description: DISSERTATION WITH DISTINCTION
URI: http://bspace.buid.ac.ae/handle/1234/49
Appears in Collections:Dissertations for Informatics (Knowledge and Data Management)

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