# Crowdsourced Task Routing via Matrix Factorization

arXiv: https://arxiv.org/abs/1310.5142 · HTML: https://mlsanity.com/paper/1310.5142/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2013.

## Built on by (most cited first, 1 of 1)

- [Towards Unbiased and Accurate Deferral to Multiple Experts](https://mlsanity.com/paper/2102.13004/) (2021)

## Abstract (arXiv)

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.