Matching and Grokking: Approaches to Personalized Crowdsourcing
Name
personalcrowds-ijcai15-v5.pdf
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1 MB
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Adobe PDF
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Author(s)
Organisciak, Peter
Teevan, Jaime
Dumais, Susan
Kalai, Adam Tauman
Miller, Robert C
Date Issued
July 2015
Journal
24th International Joint Conference on Artificial Intelligence (IJCAI 2015)
Publisher
AAAI Press / International Joint Conferences on Artificial Intelligence
Citation
Organisciak, Peter et al. "Matching and Grokking: Approaches to Personalized Crowdsourcing." 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina, July 25-31 2015, AAAI Press / International Joint Conferences on Artificial Intelligence, July 2015 © 2015 International Joint Conferences on Artificial Intelligence
Version
Author's final manuscript
Abstract
Personalization in computing helps 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 that are similar to a personalization target, and taste-grokking, where crowd workers explicitly predict the requester’s tastes. Both approaches show improvement over a non-personalized baseline, and have various benefits and drawbacks that are discussed.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://www.ijcai.org/Proceedings/15/Papers/611.pdf
