Using hierarchical skills for optimized task assignment in knowledge-intensive crowdsourcing

25th International World Wide Web Conference, WWW 2016

Volumn , Issue , 2016, Pages 843-853

  Mavridis, Panagiotis ^a   Gross Amblard, David ^a   Miklós, Zoltán ^a  

^a UNIVERSITÉ DE RENNES 1   (France)

Author keywords

Crowdsourcing; Skill modeling; Task mapping

Indexed keywords

WORLD WIDE WEB;

CROWDSOURCING PLATFORMS; HUMAN INTELLIGENCE; MODEL AND ALGORITHMS; SCIENCE PROJECTS; SIMILARITY DISTANCE; SYNTHETIC AND REAL DATA; TASK ASSIGNMENT; TASK MAPPING;

CROWDSOURCING;

EID: 85020381392     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2872427.2883070     Document Type: Conference Paper

References (27)

1. Acemoglu, D., Mostagir, M., and Ozdaglar, A. Managing innovation in a crowd. In Proceedings of the Sixteenth ACM Conference on Economics and Computation (New York, NY, USA, 2015), EC '15, ACM, pp. 283-283.
2. Amsterdamer, Y., Davidson, S. B., Milo, T., Novgorodov, S., and Somech, A. OASSIS: query driven crowd mining. In International Conference on Management of Data, SIGMOD 2014, Snowbird, UT, USA, June 22-27, 2014 (2014), pp. 589-600.
3. Bender, M. A., Farach-Colton, M., Pemmasani, G., Skiena, S., and Sumazin, P. Lowest common ancestors in trees and directed acyclic graphs. J. Algorithms 57, 2 (2005), 75-94.
4. Bozzon, A., Brambilla, M., Ceri, S., Silvestri, M., and Vesci, G. Choosing the right crowd: Expert finding in social networks. In Proceedings of the 16th International Conference on Extending Database Technology (2013), EDBT '13, pp. 637-648.
5. Bradley, K., Rafter, R., and Smyth, B. Case-based user profiling for content personalization. In Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-based Systems (2000), Springer-Verlag, pp. 62-72.
6. Campion, M. A., Fink, A. A., Ruggeberg, B. J., Carr, L., Phillips, G. M., and Odman, R. B. Doing competencies well: Best practices in competency modeling. Personnel Psychology 64, 1 (2011), 225-262.
7. Cao, C. C., She, J., Tong, Y., and Chen, L. Whom to ask?: Jury selection for decision making tasks on micro-blog services. Proc. VLDB Endow. 5, 11 (July 2012), 1495-1506.
8. Desmarais, M. C., and Baker, R. S. A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling and User-Adapted Interaction 22, 1-2 (2012), 9-38.
9. Enrich, M., Braunhofer, M., and Ricci, F. Cold-start management with cross-domain collaborative filtering and tags. In E-Commerce and Web Technologies (2013), C. Huemer and P. Lops, Eds., vol. 152 of Lecture Notes in Business Information Processing, Springer Berlin Heidelberg, pp. 101-112.
10. Fan, J., Li, G., Ooi, B. C., Tan, K.-l., and Feng, J. icrowd: An adaptive crowdsourcing framework. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (2015), SIGMOD '15, pp. 1015-1030.
11. Karger, D. R., Oh, S., and Shah, D. Budget-optimal task allocation for reliable crowdsourcing systems. Operations Research 62, 1 (February 2014), 1-24.
12. Kuhn, H. W. The hungarian method for the assignment problem. Naval Research Logistics Quarterly 2 (1955), 83-97.
13. Liu, X., Lu, M., Ooi, B. C., Shen, Y., Wu, S., and Zhang, M. Cdas: A crowdsourcing data analytics system. Proceedings of the VLDB Endowment 5, 10 (2012), 1040-1051.
14. Maarry, K., Balke, W.-T., Cho, H., Hwang, S.-w., and Baba, Y. Skill ontology-based model for quality assurance in crowdsourcing. In Database Systems for Advanced Applications, Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2014, pp. 376-387.
15. Middleton, S. E., Shadbolt, N. R., and De Roure, D. C. Ontological user profiling in recommender systems. ACM Transactions on Information Systems (TOIS) 22, 1 (2004), 54-88.
16. Mo, L., Cheng, R., Kao, B., Yang, X. S., Ren, C., Lei, S., Cheung, D. W., and Lo, E. Optimizing plurality for human intelligence tasks. In 22nd ACM International Conference on Information and Knowledge Management, CIKM'13, San Francisco, CA, USA, October 27-November 1, 2013 (2013), pp. 1929-1938.
17. Rahman, H., Thirumuruganathan, S., Roy, S. B., Amer-Yahia, S., and Das, G. Worker skill estimation in team-based tasks. PVLDB 8, 11 (2015), 1142-1153.
18. Resnik, P. Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res. (JAIR) 11 (1999), 95-130.
19. Roy, S. B., Lykourentzou, I., Thirumuruganathan, S., Amer-Yahia, S., and Das, G. Task assignment optimization in knowledge-intensive crowdsourcing. VLDB J. 24, 4 (2015), 467-491.
20. Tamir, D. 500000 worldwide mechanical turk workers. In Techlist (Retrieved September 17, 2014).
21. Victor, P., Cornelis, C., Teredesai, A. M., and De Cock, M. Whom should i trust?: The impact of key figures on cold start recommendations. In Proceedings of the 2008 ACM Symposium on Applied Computing (New York, NY, USA, 2008), SAC '08, ACM, pp. 2014-2018.
22. Vuurens, J., and de Vries, A. Obtaining High-Quality Relevance Judgments Using Crowdsourcing. IEEE Internet Computing 16, 5 (2012), 20-27.
23. Wang, D., Abdelzaher, T., Kaplan, L., and Aggarwal, C. C. Recursive fact-finding: A streaming approach to truth estimation in crowdsourcing applications. In Distributed Computing Systems (ICDCS), 2013 IEEE 33rd International Conference on (2013), IEEE, pp. 530-539.
24. Zhang, J., Tang, J., and Li, J.-Z. Expert finding in a social network. In DASFAA (2007), K. Ramamohanarao, P. R. Krishna, M. K. Mohania, and E. Nantajeewarawat, Eds., vol. 4443 of Lecture Notes in Computer Science, Springer, pp. 1066-1069.
25. Zhang, W., and Wang, J. A collective bayesian poisson factorization model for cold-start local event recommendation. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, NY, USA, 2015), KDD '15, ACM, pp. 1455-1464.
26. Zhao, Z., Cheng, J., Wei, F., Zhou, M., Ng, W., and Wu, Y. Socialtransfer: Transferring social knowledge for cold-start cowdsourcing. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (New York, NY, USA, 2014), CIKM '14, ACM, pp. 779-788.
27. Zheng, Y., Cheng, R., Maniu, S., and Mo, L. On optimality of jury selection in crowdsourcing. In Proceedings of the 18th International Conference on Extending Database Technology, EDBT 2015, Brussels, Belgium, March 23-27, 2015. (2015), pp. 193-204.