Skip navigation
Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01zs25xb89d
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorKpotufe, Samory-
dc.contributor.authorRogers, Emily-
dc.date.accessioned2016-06-24T15:16:36Z-
dc.date.available2016-06-24T15:16:36Z-
dc.date.created2016-04-12-
dc.date.issued2016-06-24-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01zs25xb89d-
dc.description.abstractWith the rise of big data comes the problem of how to properly leverage it into business insights. One area of concern is how to effectively predict customer sentiment towards products. Using matrix completion it is possible to take an incomplete matrix of users and their ratings of products and extrapolate the data to suggest new products. This problem gained considerable notoriety in the past decade with the Netflix Prize competition. However, many current methods are either over specialized by dataset, produce only theoretical results, or are overly simple. The purpose of this paper is to look at current techniques and identify an optimized method that can work on a variety of data sources.en_US
dc.format.extent65 pages*
dc.language.isoen_USen_US
dc.titleWhat to Watch: An Examination of Matrix Completion Techniques Used in the Netflix Prizeen_US
dc.typePrinceton University Senior Theses-
pu.date.classyear2016en_US
pu.departmentOperations Research and Financial Engineeringen_US
pu.pdf.coverpageSeniorThesisCoverPage-
Appears in Collections:Operations Research and Financial Engineering, 2000-2020

Files in This Item:
File SizeFormat 
RogersEmily_final_thesis.pdf3.95 MBAdobe PDF    Request a copy


Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.