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dc.contributor.authorBresler, Guy
dc.contributor.authorKarzand, Mina
dc.date.accessioned2021-11-05T13:38:28Z
dc.date.available2021-11-05T13:38:28Z
dc.date.issued2018-02
dc.identifier.urihttps://hdl.handle.net/1721.1/137451
dc.description.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.en_US
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/ita.2018.8502955en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleRegret Bounds and Regimes of Optimality for User-User and Item-Item Collaborative Filteringen_US
dc.typeArticleen_US
dc.identifier.citationBresler, Guy and Karzand, Mina. 2018. "Regret Bounds and Regimes of Optimality for User-User and Item-Item Collaborative Filtering."
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systems
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-05-10T16:20:52Z
dspace.date.submission2019-05-10T16:20:53Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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