Predicting User Behavior over the Web
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Date
2010-01
Authors
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Journal ISSN
Volume Title
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
Keywords
web usage mining, E-commerce, ECML/PKDD