Evaluation of a Similarity-based Approach to Customer-adaptive Electronic Sales Dialogs

Personalized consultation in large-scale businesses like found in electronic shops is getting more and more important. To provide customers with adequate product information, an automated, dynamic, customer-adaptive dialog has to be carried on that simulates a real sales talk between a sales person and a customer. The task of an electronic sales system is to gain very quickly as much information as possible from the customer. In this context, many approaches for dynamic question selection in automated dialogs only concentrate on dialog length using an information gain measure like the entropy approach. Neither the customers’ product knowledge nor the quality of the produced dialogs is taken into account. Here, we present a new measure for question selection in combination with consideration of the costs a question causes dependent on the respective customer class. Detailed evaluations show that this approach produces dialogs that reach the expected retrieval result with fewer questions.