Personalized human computation
Name
Miller_Personalized human.pdf
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225.77 KB
Format
Adobe PDF
Checksum (MD5)
875799c86e34f47e1a62b82d485f8146
Author(s)
Organisciak, Peter
Teevan, Jaime
Dumais, Susan
Miller, Robert C.
Kalai, Adam Tauman
Date Issued
November 2013
Journal
Proceedings of the First AAAI Conference on Human Computation and Crowdsourcing
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Organisciak, Peter, Jaime Teevan, Susan Dumais, Robert C. Miller, and Adam Tauman Kalai. "Personalized Human Computation." First AAAI Conference on Human Computation and Crowdsourcing, Palm Springs, CA, November 6-9, 2013.
Version
Author's final manuscript
Abstract
Significant effort in machine learning and information retrieval has been devoted to identifying personalized content such as recommendations and search results.
Personalized human computation has the potential to go beyond existing techniques like collaborative filtering to provide personalized results on demand, over personal data, and for complex tasks. This work-in-progress compares two approaches to personalized human computation. In both, users annotate a small set of training examples which are then used by the crowd to annotate unseen items. In the first approach, which we call taste-matching, crowd members are asked to annotate the same set of training examples, and the ratings of similar users on other items are then used to infer personalized ratings. In the second approach, taste-grokking, the crowd is presented with the training examples and asked to use them predict the ratings of the target user on other items.
MIT Department
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
http://www.aaai.org/ocs/index.php/HCOMP/HCOMP13/paper/viewFile/7551/7450
