@inproceedings{bf4a9ad44e1f4412aa55678fa8fe6f54,
title = "Reducing uncertainty of probabilistic Top-K ranking via Pairwise crowdsourcing",
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",
author = "Xin Lin and Jianliang Xu and Haibo Hu and Fan Zhe",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 34th IEEE International Conference on Data Engineering, ICDE 2018 ; Conference date: 16-04-2018 Through 19-04-2018",
year = "2018",
month = oct,
day = "24",
doi = "10.1109/ICDE.2018.00236",
language = "英语",
series = "Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1757--1758",
booktitle = "Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018",
address = "美国",
}