TY - GEN
T1 - Task composition in crowdsourcing
AU - Amer-Yahia, Sihem
AU - Gaussier, Eric
AU - Leroy, Vincent
AU - Pilourdault, Julien
AU - Borromeo, Ria Mae
AU - Toyama, Motomichi
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/22
Y1 - 2016/12/22
N2 - Crowdsourcing has gained popularity in a variety of domains as an increasing number of jobs are 'taskified' and completed independently by a set of workers. A central process in crowdsourcing is the mechanism through which workers find tasks. On popular platforms such as Amazon Mechanical Turk, tasks can be sorted by dimensions such as creation date or reward amount. Research efforts on task assignment have focused on adopting a requester-centric approach whereby tasks are proposed to workers in order to maximize overall task throughput, result quality and cost. In this paper, we advocate the need to complement that with a worker-centric approach to task assignment, and examine the problem of producing, for each worker, a personalized summary of tasks that preserves overall task throughput. We formalize task composition for workers as an optimization problem that finds a representative set of k valid and relevant Composite Tasks (CTs). Validity enforces that a composite task complies with the task arrival rate and satisfies the worker's expected wage. Relevance imposes that tasks match the worker's qualifications. We show empirically that workers' experience is greatly improved due to task homogeneity in each CT and to the adequation of CTs with workers' skills. As a result task throughput is improved.
AB - Crowdsourcing has gained popularity in a variety of domains as an increasing number of jobs are 'taskified' and completed independently by a set of workers. A central process in crowdsourcing is the mechanism through which workers find tasks. On popular platforms such as Amazon Mechanical Turk, tasks can be sorted by dimensions such as creation date or reward amount. Research efforts on task assignment have focused on adopting a requester-centric approach whereby tasks are proposed to workers in order to maximize overall task throughput, result quality and cost. In this paper, we advocate the need to complement that with a worker-centric approach to task assignment, and examine the problem of producing, for each worker, a personalized summary of tasks that preserves overall task throughput. We formalize task composition for workers as an optimization problem that finds a representative set of k valid and relevant Composite Tasks (CTs). Validity enforces that a composite task complies with the task arrival rate and satisfies the worker's expected wage. Relevance imposes that tasks match the worker's qualifications. We show empirically that workers' experience is greatly improved due to task homogeneity in each CT and to the adequation of CTs with workers' skills. As a result task throughput is improved.
KW - Clustering
KW - Crowdsourcing
KW - Fuzzy
KW - Task-composition
UR - http://www.scopus.com/inward/record.url?scp=85011298709&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85011298709&partnerID=8YFLogxK
U2 - 10.1109/DSAA.2016.27
DO - 10.1109/DSAA.2016.27
M3 - Conference contribution
AN - SCOPUS:85011298709
T3 - Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016
SP - 194
EP - 203
BT - Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016
Y2 - 17 October 2016 through 19 October 2016
ER -