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Current crowdsourcing platforms provide little support for worker feedback.Workers are sometimes invited to post free text describing their experience andpreferences in completing tasks. They can also use forums such as TurkerNation1 to exchange preferences on tasks and requesters. In fact, crowdsourcingplatforms rely heavily on observing workers and inferring their preferencesimplicitly. In this work, we believe that asking workers to indicate theirpreferences explicitly improve their experience in task completion and hence,the quality of their contributions. Explicit elicitation can indeed help tobuild more accurate worker models for task completion that captures theevolving nature of worker preferences. We design a worker model whose accuracyis improved iteratively by requesting preferences for task factors such asrequired skills, task payment, and task relevance. We propose a genericframework, develop efficient solutions in realistic scenarios, and runextensive experiments that show the benefit of explicit preference elicitationover implicit ones with statistical significance.

Eliciting Worker Preference for Task Completion