Matching and Grokking: Approaches to Personalized Crowdsourcing

Organisciak, Peter; Teevan, Jaime; Dumais, Susan T.; Miller, Robert C.; Kalai, Adam Tauman

Matching and Grokking: Approaches to Personalized Crowdsourcing

Peter Organisciak, Jaime Teevan, Susan T. Dumais, Robert C. Miller, Adam Tauman Kalai

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Best Papers from Sister Conferences Track. Pages 4296-4301.

Abstract

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 non-personalized baseline, with taste-grokking performing well in simpler tasks and taste-matching performing well with larger crowds and tasks with latent decision-making variables.

Peter Organisciak, Jaime Teevan, Susan T. Dumais, Robert C. Miller, and Adam Tauman Kalai. 2015. Matching and Grokking: Approaches to Personalized Crowdsourcing. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 4296–4301. https://ijcai.org/proceedings/2015/611

@inproceedings{ijcai2015p611,
  title     = {Matching and Grokking: Approaches to Personalized Crowdsourcing},
  author    = {Organisciak, Peter and Teevan, Jaime and Dumais, Susan T. and Miller, Robert C. and Kalai, Adam Tauman},
  booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-15}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Qiang Yang and Michael Wooldridge},
  pages     = {4296--4301},
  year      = {2015},
  month     = {7},
  note      = {Best Papers from Sister Conferences Track},
  url       = {https://ijcai.org/proceedings/2015/611},
}

TY  - CPAPER
TI  - Matching and Grokking: Approaches to Personalized Crowdsourcing
AU  - Organisciak, Peter
AU  - Teevan, Jaime
AU  - Dumais, Susan T.
AU  - Miller, Robert C.
AU  - Kalai, Adam Tauman
T2  - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2015
SP  - 4296
EP  - 4301
UR  - https://ijcai.org/proceedings/2015/611
ER  -