Skip navigation
Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01zs25xb89d
Title: What to Watch: An Examination of Matrix Completion Techniques Used in the Netflix Prize
Authors: Rogers, Emily
Advisors: Kpotufe, Samory
Department: Operations Research and Financial Engineering
Class Year: 2016
Abstract: With 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.
Extent: 65 pages
URI: http://arks.princeton.edu/ark:/88435/dsp01zs25xb89d
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Operations Research and Financial Engineering, 2000-2016

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.