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
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Machine Learning
Integrating Induction and Case-Based Reasoning: Methodological Approach and First Evaluations
EWCBR '94 Selected papers from the Second European Workshop on Advances in Case-Based Reasoning
Applying Recursive CBR for the Custumization of Structured Products in an Electronic Shop
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Personalized Conversational Case-Based Recommendation
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
A Dynamic Approach to Reducing Dialog in On-Line Decision Guides
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
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
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
simVar: A Similarity-Influenced Question Selection Criterion for e-Sales Dialogs
Artificial Intelligence Review
Exploiting Taxonomic and Causal Relations in Conversational Case Retrieval
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
HYREC: a hybrid recommendation system for e-commerce
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Completeness criteria for retrieval in recommender systems
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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For dynamic sales dialogs in electronic commerce scenarios, approaches based on an information gain measure used for attribute selection have been suggested. These measures consider the distribution of attribute values in the case base and are focused on the reduction of dialog length. The implicit knowledge contained in the similarity measures is neglected. Another important aspect that has not been investigated either is the quality of the produced dialogs, i.e. if the retrieval result is appropriate to the customer's demands. Our approach takes the more direct way to the target products by asking the attributes that induce the maximum change of similarity distribution amongst the candidate cases, thereby faster discriminating the case base in similar and dissimilar cases. Evaluations show that this approach produces dialogs that reach the expected retrieval result with fewer questions. In real world scenarios, it is possible that the customer cannot answer a question. To nevertheless reach satisfactory results, one has to balance between a high information gain and the probability that the question will not be answered. We use a Bayesian Network to estimate these probabilities.