  We describe methods to predict a crowd worker's accuracy on new tasks based
on his accuracy on past tasks. Such prediction provides a foundation for
identifying the best workers to route work to in order to maximize accuracy on
the new task. Our key insight is to model similarity of past tasks to the
target task such that past task accuracies can be optimally integrated to
predict target task accuracy. We describe two matrix factorization (MF)
approaches from collaborative filtering which not only exploit such task
similarity, but are known to be robust to sparse data. Experiments on synthetic
and real-world datasets provide feasibility assessment and comparative
evaluation of MF approaches vs. two baseline methods. Across a range of data
scales and task similarity conditions, we evaluate: 1) prediction error over
all workers; and 2) how well each method predicts the best workers to use for
each task. Results show the benefit of task routing over random assignment, the
strength of probabilistic MF over baseline methods, and the robustness of
methods under different conditions.
