C4.5: programs for machine learning
C4.5: programs for machine learning
Encouraging Experimental Results on Learning CNF
Machine Learning
Combining fuzzy information from multiple systems (extended abstract)
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Modern Information Retrieval
The use of extended Boolean logic in information retrieval
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Supporting Ranked Boolean Similarity Queries in MARS
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
An Approach to Integrating Query Refinement in SQL
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
FALCON: Feedback Adaptive Loop for Content-Based Retrieval
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Similarity Search Using Multiple Examples in MARS
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Supporting Content-based Queries over Images in MARS
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Integrating similarity retrieval and skyline exploration via relevance feedback
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
User feedback based query refinement by exploiting skyline operator
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
A framework for query refinement with user feedback
Journal of Systems and Software
On modeling query refinement by capturing user intent through feedback
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
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In numerous applications that deal with similarity search, a user may not have an exact specification of his information need and/or may not be able to formulate a query that exactly captures his notion of similarity. A promising approach to mitigate this problem is to enable the user to submit a rough approximation of the desired query and use relevance feedback on retrieved objects to refine the query. In this paper, we explore such a refinement strategy for a general class of structured similarity queries. Our approach casts the refinement problem as that of learning concepts using the tuples on which the user provides feedback as a labeled training set. Under this setup, similarity query refinement consists of two learning tasks: learning the structure of the query and learning the relative importance of query components. The paper develops machine learning approaches suitable for the two learning tasks. The primary contribution of the paper is the Refinement Activation Framework (RAF) that decides when each learner is invoked. Experimental analysis over many real life datasets shows that our strategy significantly outperforms existing approaches in terms of retrieval quality.