Abstract
User behavior profiling of websites can provide an operator with an estimate of what is actually transpiring on their site. This type of information is essential to keep ahead of the curve in a commercial environment where competition is extremely fierce and continuously evolving. The authors present an automated methodology that uses economically available web server logs to mine User Behavior Profiles (UBP) without adding significant overhead to an existing web system. They prepare user traces from the log files based on the 35 most common actions found on popular websites, and 9 user behavior profiles which describe the majority of current activity patterns identified from those sites. They classify the user trace into a UBP via a Hidden Markov Model (HMM) based classification approach. The authors applied this methodology to the logs of a virtual e-commerce website, and an industrial case study to demonstrate the validity of the proposed approach.
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