Interactive Critiquing forCatalog Navigation in E-Commerce
Artificial Intelligence Review
Comparison-Based Recommendation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Learning Feature Weights from Customer Return-Set Selections
Knowledge and Information Systems
Case-based recommender systems
The Knowledge Engineering Review
Preference-based search using example-critiquing with suggestions
Journal of Artificial Intelligence Research
The adaptive web
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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Recent research has highlighted the benefits of completeness as a retrieval criterion in recommender systems. In complete retrieval, any subset of the constraints in a given query that can be satisfied must be satisfied by at least one of the retrieved products. Minimal completeness (i.e., always retrieving the smallest set of products needed for completeness) is also beginning to attract research interest as a way to minimize cognitive load in the approach. Other important features of a retrieval algorithm's behavior include the diversity of the retrieved products and the order in which they are presented to the user. In this paper, we present a new algorithm for minimally complete retrieval (MCR) in which the ranking of retrieved products is primarily based on the number of constraints that they satisfy, but other measures such as similarity or utility can also be used to inform the retrieval process. We also present theoretical and empirical results that demonstrate our algorithm's ability to minimize cognitive load while ensuring the completeness and diversity of the retrieved products.