Collaborative future event recommendation
We demonstrate a method for collaborative ranking of future events. Previous work on recommender systems typically relies on feedback on a particular item, such as a movie, and generalizes this to other items or other people. In contrast, we examine a setting where no feedback exists on the particul...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Association for Computing Machinery
2011
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| Online Access: | http://hdl.handle.net/1721.1/62811 https://orcid.org/0000-0002-2199-0379 |
| _version_ | 1811072922063732736 |
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| author | Minkov, Einat Charrow, Ben Ledlie, Jonathan Teller, Seth Jaakkola, Tommi S. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Minkov, Einat Charrow, Ben Ledlie, Jonathan Teller, Seth Jaakkola, Tommi S. |
| author_sort | Minkov, Einat |
| collection | MIT |
| description | We demonstrate a method for collaborative ranking of future events. Previous work on recommender systems typically relies on feedback on a particular item, such as a movie, and generalizes this to other items or other people. In contrast, we examine a setting where no feedback exists on the particular item. Because direct feedback does not exist for events that have not taken place, we recommend them based on individuals' preferences for past events, combined collaboratively with other peoples' likes and dislikes. We examine the topic of unseen item recommendation through a user study of academic (scientific) talk recommendation, where we aim to correctly estimate a ranking function for each user, predicting which talks would be of most interest to them. Then by decomposing user parameters into shared and individual dimensions, we induce a similarity metric between users based on the degree to which they share these dimensions. We show that the collaborative ranking predictions of future events are more effective than pure content-based recommendation. Finally, to further reduce the need for explicit user feedback, we suggest an active learning approach for eliciting feedback and a method for incorporating available implicit user cues. |
| first_indexed | 2024-09-23T09:21:51Z |
| format | Article |
| id | mit-1721.1/62811 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:21:51Z |
| publishDate | 2011 |
| publisher | Association for Computing Machinery |
| record_format | dspace |
| spelling | mit-1721.1/628112022-09-30T14:17:42Z Collaborative future event recommendation Minkov, Einat Charrow, Ben Ledlie, Jonathan Teller, Seth Jaakkola, Tommi S. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Jaakkola, Tommi S. Jaakkola, Tommi S. Teller, Seth We demonstrate a method for collaborative ranking of future events. Previous work on recommender systems typically relies on feedback on a particular item, such as a movie, and generalizes this to other items or other people. In contrast, we examine a setting where no feedback exists on the particular item. Because direct feedback does not exist for events that have not taken place, we recommend them based on individuals' preferences for past events, combined collaboratively with other peoples' likes and dislikes. We examine the topic of unseen item recommendation through a user study of academic (scientific) talk recommendation, where we aim to correctly estimate a ranking function for each user, predicting which talks would be of most interest to them. Then by decomposing user parameters into shared and individual dimensions, we induce a similarity metric between users based on the degree to which they share these dimensions. We show that the collaborative ranking predictions of future events are more effective than pure content-based recommendation. Finally, to further reduce the need for explicit user feedback, we suggest an active learning approach for eliciting feedback and a method for incorporating available implicit user cues. Nokia Research Center 2011-05-10T20:52:20Z 2011-05-10T20:52:20Z 2010-10 Article http://purl.org/eprint/type/ConferencePaper 978-1-4503-0099-5 http://hdl.handle.net/1721.1/62811 Minkov, Einat et al. "Collaborative future event recommendation." Proceedings of the 19th ACM international conference on Information and knowledge management, Toronto, ON, Canada, 819-828, Oct. 216-30, 2010 https://orcid.org/0000-0002-2199-0379 en_US http://dx.doi.org/10.1145/1871437.1871542 Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM '10 Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Association for Computing Machinery MIT web domain |
| spellingShingle | Minkov, Einat Charrow, Ben Ledlie, Jonathan Teller, Seth Jaakkola, Tommi S. Collaborative future event recommendation |
| title | Collaborative future event recommendation |
| title_full | Collaborative future event recommendation |
| title_fullStr | Collaborative future event recommendation |
| title_full_unstemmed | Collaborative future event recommendation |
| title_short | Collaborative future event recommendation |
| title_sort | collaborative future event recommendation |
| url | http://hdl.handle.net/1721.1/62811 https://orcid.org/0000-0002-2199-0379 |
| work_keys_str_mv | AT minkoveinat collaborativefutureeventrecommendation AT charrowben collaborativefutureeventrecommendation AT ledliejonathan collaborativefutureeventrecommendation AT tellerseth collaborativefutureeventrecommendation AT jaakkolatommis collaborativefutureeventrecommendation |