Collaborative future event recommendation
Name
Jaakkola_Collaborative future.pdf
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583.72 KB
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Author(s)
Minkov, Einat
Charrow, Ben
Ledlie, Jonathan
Teller, Seth
Jaakkola, Tommi S.
Date Issued
October 2010
Journal
Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM '10
Publisher
Association for Computing Machinery
Citation
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
Version
Author's final manuscript
Abstract
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.
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 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/1871437.1871542
