Properties of extended Boolean models in information retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A formula for incorporating weights into scoring rules
Theoretical Computer Science - Special issue on the 6th International Conference on Database Theory—ICDT '97
Extended Boolean information retrieval
Communications of the ACM
Information Retrieval
Interaction Design
A Guide to the SQL Standard, 3rd Ed.; A User's Guide to the Standard Relational Lanquage SQL
A Guide to the SQL Standard, 3rd Ed.; A User's Guide to the Standard Relational Lanquage SQL
Imprecision and User Preferences in Multimedia Queries: A Generic Algebraic Approach
FoIKS '00 Proceedings of the First International Symposium on Foundations of Information and Knowledge Systems
Improving Retrieval Performance by Relevance Feedback
Improving Retrieval Performance by Relevance Feedback
Designing the User Interface: Strategies for Effective Human-Computer Interaction (4th Edition)
Designing the User Interface: Strategies for Effective Human-Computer Interaction (4th Edition)
ACM SIGMM retreat report on future directions in multimedia research
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
The VLDB Journal — The International Journal on Very Large Data Bases
A data base sublanguage founded on the relational calculus
SIGFIDET '71 Proceedings of the 1971 ACM SIGFIDET (now SIGMOD) Workshop on Data Description, Access and Control
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Current research in multimedia retrieval (MR) does not satisfactorily mirror research results from psychology revealing a different significance of certain characteristics of a media object to a query in terms of similarity. Although the relevance of user-controlled condition weights has been demonstrated, there is a lack of systems supporting users in setting these weights. In this work, we present a relevance feedback based approach that supports users to set condition weights in order to retrieve results from the MR system that are consistent with their perception of similarity. Condition weights are learned by a machine based learning algorithm from user preferences based on a partially ordered set.