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Matrix Estimation, Latent Variable Model and Collaborative Filtering
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Matrix Estimation, Latent Variable Model and Collaborative Filtering
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LIPIcs-FSTTCS-2017-4.pdf
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Published version
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419.95 KB
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sword-2019-07-10T16:22:39.original.xml (130 B)
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Author(s)
Shah, Devavrat
Date Issued
2017
Citation
Shah, Devavrat. 2017. "Matrix Estimation, Latent Variable Model and Collaborative Filtering."
Version
Final published version
Abstract
© Devavrat Shah. Estimating a matrix based on partial, noisy observations is prevalent in variety of modern applications with recommendation system being a prototypical example. The non-parametric latent variable model provides canonical representation for such matrix data when the underlying distribution satisfies “exchangeability” with graphons and stochastic block model being recent examples of interest. Collaborative filtering has been a successfully utilized heuristic in practice since the dawn of e- commerce. In this extended abstract, we will argue that collaborative filtering (and its variants) solve matrix estimation for a generic latent variable model with near optimal sample complexity.
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Terms of Use
Creative Commons Attribution 4.0 International license
https://creativecommons.org/licenses/by/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/137821
DOI of Published Version
10.4230/LIPIcs.FSTTCS.2017.4
