Eliciting Worker Preference for Task Completion
This addresses the problem of inefficient worker modeling for task completion in crowdsourcing platforms, though it is incremental as it builds on existing preference elicitation concepts.
The paper tackles the lack of worker feedback support in crowdsourcing by proposing explicit preference elicitation, showing that it improves worker experience and contribution quality with statistically significant benefits over implicit methods.
Current crowdsourcing platforms provide little support for worker feedback. Workers are sometimes invited to post free text describing their experience and preferences in completing tasks. They can also use forums such as Turker Nation1 to exchange preferences on tasks and requesters. In fact, crowdsourcing platforms rely heavily on observing workers and inferring their preferences implicitly. In this work, we believe that asking workers to indicate their preferences explicitly improve their experience in task completion and hence, the quality of their contributions. Explicit elicitation can indeed help to build more accurate worker models for task completion that captures the evolving nature of worker preferences. We design a worker model whose accuracy is improved iteratively by requesting preferences for task factors such as required skills, task payment, and task relevance. We propose a generic framework, develop efficient solutions in realistic scenarios, and run extensive experiments that show the benefit of explicit preference elicitation over implicit ones with statistical significance.