Collaborative Filtering with Low Regret
© 2016 ACM. There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we provide a framework to design and analyze various recommendation algorithms. The setup amounts to online binary matrix completion, where at each time a random...
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Association for Computing Machinery (ACM)
2021
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| Online Access: | https://hdl.handle.net/1721.1/137394 |
| _version_ | 1826216103815151616 |
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| author | Bresler, Guy Shah, Devavrat Voloch, Luis Filipe |
| author2 | Massachusetts Institute of Technology. Institute for Data, Systems, and Society |
| author_facet | Massachusetts Institute of Technology. Institute for Data, Systems, and Society Bresler, Guy Shah, Devavrat Voloch, Luis Filipe |
| author_sort | Bresler, Guy |
| collection | MIT |
| description | © 2016 ACM. There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we provide a framework to design and analyze various recommendation algorithms. The setup amounts to online binary matrix completion, where at each time a random user requests a recommendation and the algorithm chooses an entry to reveal in the user's row. The goal is to minimize regret, or equivalently to maximize the number of +1 entries revealed at any time. We analyze an item-item collaborative filtering algorithm that can achieve fundamentally better performance compared to user-user collaborative filtering. The algorithm achieves good \cold-start" performance (appropriately defined) by quickly making good recommendations to new users about whom there is little information. |
| first_indexed | 2024-09-23T16:42:22Z |
| format | Article |
| id | mit-1721.1/137394 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:42:22Z |
| publishDate | 2021 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1373942022-09-29T20:56:05Z Collaborative Filtering with Low Regret Bresler, Guy Shah, Devavrat Voloch, Luis Filipe Massachusetts Institute of Technology. Institute for Data, Systems, and Society Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science © 2016 ACM. There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we provide a framework to design and analyze various recommendation algorithms. The setup amounts to online binary matrix completion, where at each time a random user requests a recommendation and the algorithm chooses an entry to reveal in the user's row. The goal is to minimize regret, or equivalently to maximize the number of +1 entries revealed at any time. We analyze an item-item collaborative filtering algorithm that can achieve fundamentally better performance compared to user-user collaborative filtering. The algorithm achieves good \cold-start" performance (appropriately defined) by quickly making good recommendations to new users about whom there is little information. 2021-11-04T19:03:28Z 2021-11-04T19:03:28Z 2016-06-14 2019-05-10T16:12:50Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137394 Bresler, Guy, Shah, Devavrat and Voloch, Luis Filipe. 2016. "Collaborative Filtering with Low Regret." en 10.1145/2896377.2901469 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) Other repository |
| spellingShingle | Bresler, Guy Shah, Devavrat Voloch, Luis Filipe Collaborative Filtering with Low Regret |
| title | Collaborative Filtering with Low Regret |
| title_full | Collaborative Filtering with Low Regret |
| title_fullStr | Collaborative Filtering with Low Regret |
| title_full_unstemmed | Collaborative Filtering with Low Regret |
| title_short | Collaborative Filtering with Low Regret |
| title_sort | collaborative filtering with low regret |
| url | https://hdl.handle.net/1721.1/137394 |
| work_keys_str_mv | AT breslerguy collaborativefilteringwithlowregret AT shahdevavrat collaborativefilteringwithlowregret AT volochluisfilipe collaborativefilteringwithlowregret |