BTR: A Feature-Based Bayesian Task Recommendation Scheme for Crowdsourcing System

Wei Dai, Yufeng Wang, Jianhua Ma, Qun Jin

Research output: Contribution to journalArticle

Abstract

The crowdsourcing system is a distributed problem-solving platform, in which tasks are delivered to the crowd (i.e., crowdworkers) in the form of an open call. Usually, large-scale crowdsourcing systems contain abundant microtasks, and the overhead of a crowdworker spending on searching the appropriate task may be comparable to the cost of completing the task. Therefore, task recommendation is necessary. However, existing work ignores the dynamics in crowdsourcing system, i.e., new tasks continually arrive, which leads to the issues of task cold-start. To overcome the challenge of the new coming task recommendation, this article proposes a feature-based Bayesian task recommendation (BTR) scheme. The key idea to deal with the dynamics of the crowdsourcing system lies in that the BTR learns the latent factor of the task through the task features instead of task ID and then learns the user's preference according to their historical behaviors. Specifically, based on task features and the user's historical behavior records, BTR can not only timely provide crowdworkers with personalized task recommendations but also solve the task cold-start problem. The simulations based on the real crowdsourced data set demonstrate that BTR performs better than other typical schemes that target at recommending the newly arrived tasks to crowdworkers.

Original languageEnglish
Article number9076653
Pages (from-to)780-789
Number of pages10
JournalIEEE Transactions on Computational Social Systems
Volume7
Issue number3
DOIs
Publication statusPublished - 2020 Jun

Keywords

  • Crowdsourcing system
  • neural network
  • task cold start
  • task recommendation

ASJC Scopus subject areas

  • Modelling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction

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