Regret Bounds and Regimes of Optimality for User-User and Item-Item Collaborative Filtering
Name
1711.02198.pdf
Description
Accepted version
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530.18 KB
Format
Adobe PDF
Checksum (MD5)
c94505b3aefdd7f4e116836bec2ac5f2
Author(s)
Bresler, Guy
Karzand, Mina
Date Issued
February 2018
Publisher
IEEE
Citation
Bresler, Guy and Karzand, Mina. 2018. "Regret Bounds and Regimes of Optimality for User-User and Item-Item Collaborative Filtering."
Version
Author's final manuscript
Abstract
© 2018 IEEE. We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing 'like' or 'dislike' feedback. A latent variable model specifies the user preferences: both users and items are clustered into types. All users of a given type have identical preferences for the items, and similarly, items of a given type are either all liked or all disliked by a given user. The model captures structure in both the item and user spaces, and in this paper we assume that the type preference matrix is randomly generated. We describe two algorithms inspired by user-user and item-item collaborative filtering (CF), modified to explicitly make exploratory recommendations, and prove performance guarantees in terms of their expected regret. For two regimes of model parameters, with structure only in item space or only in user space, we prove information-theoretic lower bounds on regret that match our upper bounds up to logarithmic factors. Our analysis elucidates system operating regimes in which existing CF algorithms are nearly optimal.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ita.2018.8502955
