Personalized human computation
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 re...
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for the Advancement of Artificial Intelligence
2014
|
| Online Access: | http://hdl.handle.net/1721.1/90817 https://orcid.org/0000-0002-0442-691X |
| _version_ | 1811082244886888448 |
|---|---|
| author | Organisciak, Peter Teevan, Jaime Dumais, Susan Miller, Robert C. Kalai, Adam Tauman |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Organisciak, Peter Teevan, Jaime Dumais, Susan Miller, Robert C. Kalai, Adam Tauman |
| author_sort | Organisciak, Peter |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:59:42Z |
| format | Article |
| id | mit-1721.1/90817 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:59:42Z |
| publishDate | 2014 |
| publisher | Association for the Advancement of Artificial Intelligence |
| record_format | dspace |
| spelling | mit-1721.1/908172022-09-27T23:20:39Z Personalized human computation Organisciak, Peter Teevan, Jaime Dumais, Susan Miller, Robert C. Kalai, Adam Tauman Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Miller, Robert C. 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. 2014-10-09T13:27:04Z 2014-10-09T13:27:04Z 2013-11 Article http://purl.org/eprint/type/ConferencePaper ISBN: 978-1-57735-631-8 http://hdl.handle.net/1721.1/90817 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. https://orcid.org/0000-0002-0442-691X en_US http://www.aaai.org/ocs/index.php/HCOMP/HCOMP13/paper/viewFile/7551/7450 Proceedings of the First AAAI Conference on Human Computation and Crowdsourcing Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for the Advancement of Artificial Intelligence MIT web domain |
| spellingShingle | Organisciak, Peter Teevan, Jaime Dumais, Susan Miller, Robert C. Kalai, Adam Tauman Personalized human computation |
| title | Personalized human computation |
| title_full | Personalized human computation |
| title_fullStr | Personalized human computation |
| title_full_unstemmed | Personalized human computation |
| title_short | Personalized human computation |
| title_sort | personalized human computation |
| url | http://hdl.handle.net/1721.1/90817 https://orcid.org/0000-0002-0442-691X |
| work_keys_str_mv | AT organisciakpeter personalizedhumancomputation AT teevanjaime personalizedhumancomputation AT dumaissusan personalizedhumancomputation AT millerrobertc personalizedhumancomputation AT kalaiadamtauman personalizedhumancomputation |