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http://hdl.handle.net/10397/95619
Title: | Reducing uncertainty of probabilistic top-k ranking via pairwise crowdsourcing | Authors: | Lin, X Xu, J Hu, H Zhe, F |
Issue Date: | Apr-2018 | Source: | 2018 IEEE 34th International Conference on Data Engineering (ICDE), 16-19 April 2018, Paris, France | Abstract: | In this paper, we propose a novel pairwise crowd-sourcing model to reduce the uncertainty of top-k ranking using a crowd of domain experts. Given a crowdsourcing task of limited budget, we propose efficient algorithms to select the best object pairs for crowdsourcing that will bring in the highest quality improvement. Extensive experiments show that our proposed solutions outperform a random selection method by up to 30 times in terms of quality improvement of probabilistic top- k ranking queries. In terms of efficiency, our proposed solutions can reduce the elapsed time of a brute-force algorithm from several days to one minute. | Keywords: | Crowdsourcing Top k Uncertain query |
Publisher: | IEEE | ISBN: | 978-1-5386-5520-7 | DOI: | 10.1109/ICDE.2018.00236 | Rights: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication X. Lin, J. Xu, H. Hu and F. Zhe, "Reducing Uncertainty of Probabilistic Top-k Ranking via Pairwise Crowdsourcing," 2018 IEEE 34th International Conference on Data Engineering (ICDE), 2018, pp. 1757-1758 is available at https://doi.org/10.1109/ICDE.2018.00236 |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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Reducing_Uncertainty_Probabilistic.pdf | Pre-Published version | 1.92 MB | Adobe PDF | View/Open |
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