Collaborative Filtering with Low Regret
Name
sigm122-breslerA.pdf
Description
Accepted version
Size
846.41 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Bresler, Guy
Shah, Devavrat
Voloch, Luis Filipe
Date Issued
June 14, 2016
Publisher
Association for Computing Machinery (ACM)
Citation
Bresler, Guy, Shah, Devavrat and Voloch, Luis Filipe. 2016. "Collaborative Filtering with Low Regret."
Version
Author's final manuscript
Abstract
© 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.
MIT Department
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
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2896377.2901469
