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dc.contributor.authorBorgs, Christian
dc.contributor.authorChayes, Jennifer T
dc.contributor.authorShah, Devavrat
dc.contributor.authorYu, Christina Lee
dc.date.accessioned2022-07-20T13:10:19Z
dc.date.available2022-07-20T13:10:19Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/143872
dc.description.abstract<jats:p> Matrix estimation or completion has served as a canonical mathematical model for recommendation systems. More recently, it has emerged as a fundamental building block for data analysis as a first step to denoise the observations and predict missing values. Since the dawn of e-commerce, similarity-based collaborative filtering has been used as a heuristic for matrix etimation. At its core, it encodes typical human behavior: you ask your friends to recommend what you may like or dislike. Algorithmically, friends are similar “rows” or “columns” of the underlying matrix. The traditional heuristic for computing similarities between rows has costly requirements on the density of observed entries. In “Iterative Collaborative Filtering for Sparse Matrix Estimation” by Christian Borgs, Jennifer T. Chayes, Devavrat Shah, and Christina Lee Yu, the authors introduce an algorithm that computes similarities in sparse datasets by comparing expanded local neighborhoods in the associated data graph: in effect, you ask friends of your friends to recommend what you may like or dislike. This work provides bounds on the max entry-wise error of their estimate for low rank and approximately low rank matrices, which is stronger than the aggregate mean squared error bounds found in classical works. The algorithm is also interpretable, scalable, and amenable to distributed implementation. </jats:p>en_US
dc.language.isoen
dc.publisherInstitute for Operations Research and the Management Sciences (INFORMS)en_US
dc.relation.isversionof10.1287/OPRE.2021.2193en_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.titleIterative Collaborative Filtering for Sparse Matrix Estimationen_US
dc.typeArticleen_US
dc.identifier.citationBorgs, Christian, Chayes, Jennifer T, Shah, Devavrat and Yu, Christina Lee. 2021. "Iterative Collaborative Filtering for Sparse Matrix Estimation." Operations Research.
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society
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.departmentStatistics and Data Science Center (Massachusetts Institute of Technology)
dc.relation.journalOperations Researchen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-07-20T12:52:15Z
dspace.orderedauthorsBorgs, C; Chayes, JT; Shah, D; Yu, CLen_US
dspace.date.submission2022-07-20T12:52:16Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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