Abstract
Similarity retrieval have been widely used in many practical search applications. A similarity query model can be viewed as a logical combination of a set of similarity predicates. A user can initialize a query model, but model parameters or the model itself may be inadequately specified. As a result, a retrieval system cannot guarantee that it has presented all the relevant tuples to the user. In this paper, we propose a framework that integrates the similarity retrieval and skyline exploration. Using the relevance feedback as a way to constrain the search space, our framework can intelligently explore only a necessary portion of data that contains all the relevant tuples. Our framework is also flexible enough to incorporate model refinement techniques to retrieving relevant results as early as possible.
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© 2007 Springer-Verlag Berlin Heidelberg
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Ma, Y., Mehrotra, S. (2007). Integrating Similarity Retrieval and Skyline Exploration Via Relevance Feedback. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds) Advances in Databases: Concepts, Systems and Applications. DASFAA 2007. Lecture Notes in Computer Science, vol 4443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71703-4_101
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DOI: https://doi.org/10.1007/978-3-540-71703-4_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71702-7
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