Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem
This work addresses quality control issues for experimenters using crowdsourcing platforms, such as in search engine relevance evaluation, but is incremental as it builds on existing bandit problem concepts.
The authors tackled the problem of quality control in crowdsourced multiple-choice tasks by proposing the bandit survey problem model and developing algorithms for adaptive quality control, supported by analysis and simulations.
Very recently crowdsourcing has become the de facto platform for distributing and collecting human computation for a wide range of tasks and applications such as information retrieval, natural language processing and machine learning. Current crowdsourcing platforms have some limitations in the area of quality control. Most of the effort to ensure good quality has to be done by the experimenter who has to manage the number of workers needed to reach good results. We propose a simple model for adaptive quality control in crowdsourced multiple-choice tasks which we call the \emph{bandit survey problem}. This model is related to, but technically different from the well-known multi-armed bandit problem. We present several algorithms for this problem, and support them with analysis and simulations. Our approach is based in our experience conducting relevance evaluation for a large commercial search engine.