Temporary user modeling for adaptive product presentations in the Web

This work proposes a temporary user modeling approach that enables the immediate adaptation of product presentations in the Web to the individual customer at runtime by using a machine learning algorithm. We focus on the question of how monitored user interactions can be preprocessed to get example data for the learning algorithm and how existing decision tree or decision list algorithms fit the requirements of the new application field of electronic shopping.