Case-based reasoning
A personalized television listings service
Communications of the ACM
Information Retrieval
Developing Industrial Case-Based Reasoning Applications: The Inreca Methodology
Developing Industrial Case-Based Reasoning Applications: The Inreca Methodology
An Expressive Query Language for Product Recommender Systems
Artificial Intelligence Review
Precision and Recall in Interactive Case-Based Reasoning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Refining Conversational Case Libraries
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Intelligent Sales Support with CBR
Case-Based Reasoning Technology, From Foundations to Applications
Simplifying decision trees: A survey
The Knowledge Engineering Review
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
Horizontal Case Representation
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Proceedings of the 2008 ACM conference on Recommender systems
ICCBR'03 Proceedings of the 5th 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
A method for determining ontology-based semantic relevance
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
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Recommender systems for helping users to selectfrom available products or services areincreasingly common in electronic commerce. Typically in case-based reasoning (CBR)approaches to product recommendation, the itemsrecommended are those that are most similar toa target query representing the elicitedrequirements of the user. Usually in practice,the user is required to specify a singlepreferred value for each attribute in thequery. However, we argue that a more flexibleapproach to requirements elicitation isnecessary to meet the needs of different users,ranging from those who know exactly what theyare looking for to those whose requirements arevague in the extreme. We show how the standardapproach to similarity-based retrieval can begeneralised to support queries in which theuser can enter any number of preferred valuesof a selected attribute, and examine thepotential benefits of the approach.