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Big Data Platforms
NETFLIX
Recommender Systems
Abhi Ghose, Akarsh Sahu, Markus Wehr
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Outline
▪ Business Problem
▪ Methodology
▪ Data Sources
▪ Data Visualizations
▪ Models
▪ Evaluation
▪ Customer Profiles
▪ Recommendation Visualizations
▪ Challenges & Scope for Improvements
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Business Problem
…RECOMMENDATION SYSTEM CRUCIAL TO NETFLIX’ SUCCESS!
The longer people watch:
▪ The lower churn rate (increase revenue from membership fees)
▪ The more placements can be shown per person (potential revenue + reduced marketing expenses)
[1] Form 10K Q4 2018.
[2]https://www.fastcompany.com/90380266/more-product-placements-may-come-to-
netflix-but-dont-call-them-ads
[3] https://www.businessofapps.com/data/netflix-statistics/
Netflix Revenue Streams:
▪ Membership fees ($ 7.6B domestic, $ 7.8B international, $ 0.36B DVD
domestic)
[1]
▪ Potential future streams: Ad-placement (e.g., Stranger Things season 3
alone had placements worth ~$ 15M)
[2]
▪ Placements also help to reduce marketing expenses up to $ 1B per year
[3]
(e.g. KFC advertised Stranger Things, because their products appear in
season 2)
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Methodology
Data
Aggregation
Exploratory
Data
Analysis
Data
Preprocess
k-means
ALS
Model
Evaluation
Recommendation
Visualizations
Clusters
Watch History
Recommendation
Top 200
Actors
Top 200
Directors
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Data Sources
Data Sources Data Structure Combined Size Processed # Files
IMDB Database Individual .csv files for
genres, actors, directors,
ratings, etc.
6 GB
21 GB master dataset
(6 distributed clusters on
RCC Hadoop)
7
Netflix Database 4 combined .txt files with
single row for each
customer
4 GB 4
Top 200 Actors Oscar winning popular
actors .csv file
5 KB 1
Top 100 Directors Oscar winning popular
directors .csv file
5 KB 1
