Prediction of Hourly Earnings and Completion Time on a Crowdsourcing Platform
| Autor: | Anna Lioznova, Anastasia Bezzubtseva, Alexey Drutsa, Vladimir Kukushkin |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Earnings
business.industry Computer science media_common.quotation_subject 02 engineering and technology Crowdsourcing Machine learning computer.software_genre Learning effect Task (project management) Test (assessment) Incentive 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Artificial intelligence business Baseline (configuration management) computer media_common |
| Zdroj: | KDD |
| DOI: | 10.1145/3394486.3403369 |
| Popis: | We study the problem of predicting future hourly earnings and task completion time for a crowdsourcing platform user who sees the list of available tasks and wants to select one of them to execute. Namely, for each task shown in the list, one needs to have an estimated value of the user's performance (i.e., hourly earnings and completion time) that will be if she selects this task. We address this problem on real crowd tasks completed on one of the global crowdsourcing marketplaces by (1) conducting a survey and an A/B test on real users; the results confirm the dominance of monetary incentives and importance of knowledge on hourly earnings for users; (2) an in-depth analysis of user behavior that shows that the prediction problem is challenging: (a) users and projects are highly heterogeneous, (b) there exists the so-called "learning effect" of a user selected a new task; and (3) the solution to the problem of predicting user performance that demonstrates improvement of prediction quality by up to 25% for hourly earnings and up to $32%$ completion time w.r.t. a naive baseline which is based solely on historical performance of users on tasks. In our experimentation, we use data about 18 million real crowdsourcing tasks performed by $161$ thousand users on the crowd platform; we publish this dataset. The hourly earning prediction has been deployed in Yandex.Toloka. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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