Learning about media Users from movie rating data
Name
1227275102-MIT.pdf
Size
1.92 MB
Format
Adobe PDF
Checksum (MD5)
525b213de9cfd800193162d1b277ec01
Author(s)
Chen, Lantian,M. Eng.Massachusetts Institute of Technology.
Advisor(s)
Phillip Isola.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we study techniques of machine learning for media users who submitted movie ratings to the MovieLens dataset --- a project inspired by Sky UK's own business problems I encountered during my internship there. It follows the "feature engineering" paradigm, compared to the "deep learning" paradigm, through three stages: Feature Engineering, Clustering and Recommendation, each being a classic machine learning problem. For each step, I am introducing the common, relevant methods, along with my own designed models on top of available tools and experiments on the MovieLens data on the Google Cloud Platform. Due to the open-ended nature of all three problems, we don't have quantifiable conclusions on which methods would prove the best; instead, presented here is some learning on the trade-offs and suitability for these designs.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (page 29).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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