Abstract
Measuring workers’ abilities is a way to address the long standing problem of quality control in crowdsourcing. The approaches for measuring worker ability reported in recent work can be classified into two groups, i.e., upper bound-based approaches and lower bound-based approaches. Most of these works are based on two assumptions: (1) workers give their answers to a task independently and are not affected by other workers; (2) a worker’s ability for a task is a fixed value. However realistically, a worker’s ability should be evaluated as a relative value to those of others within a group. In this work, we propose an approach called GroExpert to identify experts based on their relative values in their working groups, which can be used as a basis for quality estimation in crowdsourcing. The proposed solution employs a fully connected neural network to implement the pairwise ranking method when identifying experts. Both workers’ features and groups’ features are considered in GroExpert. We conduct a set of experiments on three real-world datasets from the Amazon Mechanical Turk platform. The experimental results show that the proposed GroExpert approach outperforms the state-of-the-art in worker ability measurement.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Allahbakhsh, M., Ignjatovic, A., Benatallah, B., Bertino, E., Foo, N., et al.: Reputation management in crowdsourcing systems. In: CollaborateCom, pp. 664–671. IEEE (2012)
Burges, C., et al.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 89–96. ACM (2005)
Chollet, F., et al.: Keras (2015). https://keras.io
Daniel, F., Kucherbaev, P., Cappiello, C., Benatallah, B., Allahbakhsh, M.: Quality control in crowdsourcing: a survey of quality attributes, assessment techniques, and assurance actions. ACM Comput. Surv. (CSUR) 51(1), 7 (2018)
Dawid, A.P., Skene, A.M.: Maximum likelihood estimation of observer error-rates using the EM algorithm. Appl. Stat. 28, 20–28 (1979)
Donmez, P., Carbonell, J.G.: Proactive learning: cost-sensitive active learning with multiple imperfect oracles. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 619–628. ACM (2008)
Fan, J., Li, G., Ooi, B.C., Tan, K.l., Feng, J.: iCrowd: an adaptive crowdsourcing framework. In: Proceedings of the 2015 ACM SIGMOD, pp. 1015–1030. ACM (2015)
Hirth, M., Hoßfeld, T., Tran-Gia, P.: Cheat-detection mechanisms for crowdsourcing. University of Würzburg, Technical report, vol. 4 (2010)
Hu, H., Zheng, Y., Bao, Z., Li, G., Feng, J., Cheng, R.: Crowdsourced poi labelling: location-aware result inference and task assignment. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 61–72. IEEE (2016)
Jagabathula, S., Subramanian, L., Venkataraman, A.: Reputation-based worker filtering in crowdsourcing. In: Advances in Neural Information Processing Systems, pp. 2492–2500 (2014)
Jain, A., Sarma, A.D., Parameswaran, A., Widom, J.: Understanding workers, developing effective tasks, and enhancing marketplace dynamics: a study of a large crowdsourcing marketplace. Proc. VLDB Endow. 10(7), 829–840 (2017)
Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD, pp. 133–142. ACM (2002)
Li, G., Wang, J., Zheng, Y., Franklin, M.J.: Crowdsourced data management: a survey. IEEE Trans. Knowl. Data Eng. 28(9), 2296–2319 (2016)
Li, H., Zhao, B., Fuxman, A.: The wisdom of minority: discovering and targeting the right group of workers for crowdsourcing. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 165–176. ACM (2014)
Li, J., Baba, Y., Kashima, H.: Hyper questions: unsupervised targeting of a few experts in crowdsourcing. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1069–1078. ACM (2017)
Liu, Q., Peng, J., Ihler, A.T.: Variational inference for crowdsourcing. In: Advances in Neural Information Processing Systems, pp. 692–700 (2012)
Ma, F., et al.: Faitcrowd: fine grained truth discovery for crowdsourced data aggregation. In: Proceedings of the 21th ACM SIGKDD, pp. 745–754. ACM (2015)
Sheng, V.S., Provost, F., Ipeirotis, P.G.: Get another label? Improving data quality and data mining using multiple, noisy labelers. In: Proceedings of the 14th ACM SIGKDD, pp. 614–622. ACM (2008)
Sunahase, T., Baba, Y., Kashima, H.: Pairwise hits: quality estimation from pairwise comparisons in creator-evaluator crowdsourcing process. In: AAAI, pp. 977–984 (2017)
Venanzi, M., Guiver, J., Kazai, G., Kohli, P., Shokouhi, M.: Community-based Bayesian aggregation models for crowdsourcing. In: Proceedings of the 23rd WWW, pp. 155–164. ACM (2014)
Venanzi, M., Rogers, A., Jennings, N.R.: Trust-based fusion of untrustworthy information in crowdsourcing applications. In: Proceedings of the 2013 International Conference on Autonomous Agents and Multi-Agent Systems, pp. 829–836. International Foundation for Autonomous Agents and Multiagent Systems (2013)
Welinder, P., Branson, S., Perona, P., Belongie, S.J.: The multidimensional wisdom of crowds. In: Advances in Neural Information Processing Systems, pp. 2424–2432 (2010)
Yin, L., Han, J., Zhang, W., Yu, Y.: Aggregating crowd wisdoms with label-aware autoencoders. In: Proceedings of the 26th IJCAI, pp. 1325–1331. AAAI Press (2017)
Yu, H., Shen, Z., Miao, C., An, B.: Challenges and opportunities for trust management in crowdsourcing. In: IEEE/WIC/ACM, pp. 486–493. IEEE Computer Society (2012)
Zhang, T.: Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 116. ACM (2004)
Zheng, Y., Li, G., Cheng, R.: DOCS: a domain-aware crowdsourcing system using knowledge bases. Proc. VLDB Endow. 10(4), 361–372 (2016)
Zheng, Y., Li, G., Li, Y., Shan, C., Cheng, R.: Truth inference in crowdsourcing: is the problem solved? Proc. VLDB Endow. 10(5), 541–552 (2017)
Acknowledgements
This work was supported in part by the MQNS (No. 9201701203), the MQEPS (No. 96804590), the MQRSG (No. 95109718), and in part by the Investigative Analytics Collaborative Research Project between Macquarie University and Data61 CSIRO.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Xing, Q., Zhao, W., Yang, J., Wu, J., Wang, Q., Wang, M. (2019). GroExpert: A Novel Group-Aware Experts Identification Approach in Crowdsourcing. In: Cheng, R., Mamoulis, N., Sun, Y., Huang, X. (eds) Web Information Systems Engineering – WISE 2019. WISE 2020. Lecture Notes in Computer Science(), vol 11881. Springer, Cham. https://doi.org/10.1007/978-3-030-34223-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-34223-4_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34222-7
Online ISBN: 978-3-030-34223-4
eBook Packages: Computer ScienceComputer Science (R0)