Diversity based Relevance Feedback for Time Series Search

Date
2013
Authors
Eravci, B.
Ferhatosmanoglu H.
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Source Title
Proceedings of the VLDB Endowment
Print ISSN
21508097
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Volume
7
Issue
2
Pages
109 - 120
Language
English
Type
Article
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Abstract

We propose a diversity based relevance feedback approach for time series data to improve the accuracy of search results. We first develop the concept of relevance feedback for time series based on dual-tree complex wavelet (CWT) and SAX based approaches. We aim to enhance the search quality by incorporating diversity in the results presented to the user for feedback. We then propose a method which utilizes the representation type as part of the feedback, as opposed to a human choosing based on a preprocessing or training phase. The proposed methods utilize a weighting to handle the relevance feedback of important properties for both single and multiple representation cases. Our experiments on a large variety of time series data sets show that the proposed diversity based relevance feedback improves the retrieval performance. Results confirm that representation feedback incorporates item diversity implicitly and achieves good performance even when using simple nearest neighbor as the retrieval method. To the best of our knowledge, this is the first study on diversification of time series search to improve retrieval accuracy and representation feedback. © 2013 VLDB Endowment.

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Keywords
Dual-tree complex wavelets, Multiple representation, Nearest neighbors, Relevance feedback, Representation type, Retrieval accuracy, Retrieval performance, Time series searches, Information retrieval, Time series, Wavelet transforms, Feedback
Citation
Published Version (Please cite this version)