Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Generating queries and replies during information-seeking interactions
International Journal of Human-Computer Studies
Developing Industrial Case-Based Reasoning Applications: The Inreca Methodology
Developing Industrial Case-Based Reasoning Applications: The Inreca Methodology
A Comparison of Incremental Case-Based Reasoning and Inductive Learning
EWCBR '94 Selected papers from the Second European Workshop on Advances in Case-Based Reasoning
Reuse of Complex Electronic Designs: Requirements Analysis for a CBR Application
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
A Similarity-Based Approach to Attribute Selection in User-Adaptive Sales Dialogs
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
INRECA: A Seamlessly Integrated System Based on Inductive Inference and Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Intelligent Sales Support with CBR
Case-Based Reasoning Technology, From Foundations to Applications
minimizing dialog length in interactive case-based reasoning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
ExpertClerk: navigating shoppers' buying process with the combination of asking and proposing
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Acquiring Customers' Requirementsin Electronic Commerce
Artificial Intelligence Review
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Supporting product selection with query editing recommendations
Proceedings of the 2007 ACM conference on Recommender systems
Enhancing the diversity of conversational collaborative recommendations: a comparison
Artificial Intelligence Review
Dynamic active probing of helpdesk databases
Proceedings of the VLDB Endowment
The adaptive web
Experience management: foundations, development methodology, and internet-based applications
Experience management: foundations, development methodology, and internet-based applications
A knowledge-intensive method for conversational CBR
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Semi-automatic generation of recommendation processes and their GUIs
Proceedings of the 2013 international conference on Intelligent user interfaces
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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.