In the Web environment, especially for e-commerce sites, user identification is becoming a major challenge for admission control on high traffic sites. As previously demonstrated by several authors [5, 2] , when a web server is overloaded there is a significant loss of throughput when we compare finished sessions and the number of responses per second; longer sessions are usually the ones ending in sales but also the most sensitive to load failures. Although there is some work on session-based admission control [2, 13] , this approach does not maximize revenue as it treats all non-logged sessions the same. In this paper we present a novel method for learning to assign priorities to users with higher purchasing intentions. For this, we use traditional machine learning techniques and second order Markov-chain models; we test our approach on access logs obtained from a high-traffic online travel agency. We are able to train a system to estimate the probability of the possible user's purchasing intentions according to its early navigation clicks and other static information. The predictions can be used by admission control systems to prioritize sessions or deny them if no resources are available, thus improving sales throughput per unit of time.
An architecture for admission control on e-commerce websites that prioritizes user sessions based on predictions about the user's intentions to buy a product is proposed in REF .
Web Customer Modeling for Automated Session Prioritization on High Traffic Sites
Proceedings of the 11th International Conference on User Modeling. Corfu
Computer Science