ICEC '04 Proceedings of the 6th international conference on Electronic commerce
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
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Traditionally, the study of on-line dynamic pricing and bundling strategies for information goods is motivated by the value-extracting or profit-generating potential of these strategies. In this paper we discuss the relatively overlooked potential of these strategies to on-line learn more about customers' preferences. Based on this enhanced customer knowledge an information broker can 驴 by tailoring the brokerage services more to the demand of the various customer groups 驴 persuade customers to engage in repeated transactions (i.e., generate customer lock-in). To illustrate the discussion, we show by means of a basic consumer model how, with the use of on-line dynamic bundling and pricing algorithms, customer lock-in can occur. The lock-in occurs because the algorithms can both find appropriate prices and (from the customers' perspective) the most interesting bundles. In the conducted computer experiments we use an advanced genetic algorithm with a niching method to learn the most interesting bundles efficiently and effectively. brokerage; recommender systems.