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Title:
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THE PREDICTION OF WEB USER TASKS BY ANALYZING CLIENT LOGS |
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Author(s):
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Anne Gutschmidt |
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ISBN:
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978-972-8924-68-3 |
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Editors:
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Pedro Isaías, Miguel Baptista Nunes and Dirk Ifenthaler |
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Year:
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2008 |
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Edition:
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Single |
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Keywords:
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user tasks, user behavior, event logs, exploratory study, empirical research |
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Type:
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Full Paper |
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First Page:
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136 |
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Last Page:
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143 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Long-term user preferences are a common target of personalized Web content and interfaces. However, it is also the
user's information need at the very moment which is of essential importance. This paper deals with the recognition of a
person's current task by the surfing behavior. The surfing behavior is represented by several attributes derived from an
event log which contains user actions, such as mouse and scroll moves etc. The challenge is to select those attributes
which best describe the behavior such that a conclusion on the user's task is possible. A pilot study was conducted where
users performed exercises, each corresponding to one of the predefined user tasks Fact Finding, Information Gathering
and Just Browsing. The detailed event logs we gained from the experiments allowed the consideration of stronger
behavioral features which, moreover, formed a novel composition of attributes. Decision trees and Naive Bayes
classification were applied on this composition, leading to accuracy values of up to 95% of correctly identified user tasks. |
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