Collaborative Filtering with Low Regret

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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Bibliographic Details
Main Authors: Bresler, Guy (Author), Shah, Devavrat (Author), Voloch, Luis Filipe (Author)
Other Authors: Massachusetts Institute of Technology. Institute for Data, Systems, and Society (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Association for Computing Machinery (ACM), 2021-11-04T19:03:28Z.
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