Skip to main content
Log in
(current)
MIT Open Scholarship
DSpace@MIT
Search
Home
MIT Open Access Articles
MIT Open Access Articles
Matrix Estimation, Latent Variable Model and Collaborative Filtering
This is not the latest version of this item. The latest version can be found
here
.
Matrix Estimation, Latent Variable Model and Collaborative Filtering
Download
Name
LIPIcs-FSTTCS-2017-4.pdf
Description
Published version
Show more
Size
419.95 KB
Format
Adobe PDF
Checksum
(MD5)
5b47875b1cd2e9ebc0ba6969109985c5
Show more
sword-2019-07-10T16:22:39.original.xml (130 B)
Original SWORD entry document
Show more
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.
Show more
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
