simVar: A Similarity-Influenced Question Selection Criterion for e-Sales Dialogs

摘要

Even though AI technologies like CBR have proved their strengths for intelligent sales support in EC applications, on-line customers often encounter e-sales systems that are hard to use. Before a search process is started, they either have to answer many annoying or irrelevant questions or they are faced with technical jargon of manufacturers they are not able to understand. On-line customers want personalised advice and adequate product offerings. Gaining sufficient information from the customer but also providing her with information at the right place is the key. Resulting from this fact, an automated communication process is needed that simulates the sales dialog between customers and human sales persons. This article proposes a method for question selection in e-sales dialogs based on the variance of the CBR system's inherent similarities. The method uses a similarity-influenced measure to reduce the number of questions required to find satisfactory products. Additionally, it is shown how questions can be selected on the level of abstraction appropriate to the customer's knowledge.