Matching and Grokking: Approaches to Personalized Crowdsourcing

Personalization aims to tailor content to a person's individual tastes. As a result, the tasks that benefit from personalization are inherently subjective. Many of the most robust approaches to personalization rely on large sets of other people's preferences. However, existing preference data is not always available. In these cases, we propose leveraging online crowds to provide on-demand personalization. We introduce and evaluate two methods for personalized crowdsourcing: taste-matching for finding crowd workers who are similar to the requester, and taste-grokking, where crowd workers explicitly predict the requester's tastes. Both approaches show improvement over a nonpersonalized baseline, with taste-grokking performing well in simpler tasks and taste-matching performing well with larger crowds and tasks with latent decision-making variables.