- Rogers, Emily [Browse]
- Senior thesis
- 65 pages
- Kpotufe, Samory [Browse]
- Princeton University. Department of Operations Research and Financial Engineering [Browse]
- Class year
- Summary note
- 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.