Presenter: Kyle Napierkowski, Sr. Director of Product Management, RadiumOne

1. Graph-based Feature
Extraction for Online
Advertising Prediction
Kyle Napierkowski
RadiumOne

2. Agenda
• The online advertising prediction problem
• The User-Domain matrix as a graph
• PageRank
• K-Core
• Clustering with community detection
• Conclusions
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3. Online Advertising Prediction
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Browsing
Visit Advertiser
Site
Buy Product
IN A NUTSHELL:
Predict a user’s interest in buying products based on browsing
behavior

4. The User-Domain Matrix
Represent m users’ visits to n web domains as a matrix
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a.co
m
b.co
m
… n
1 0 0 … 1
2 0 0 … 0
… … … … …
m 0 1 … 1
m = 1
billion
users
n = 500,000 domains How do we make
sense of user
interactions across
sites?
How do we turn very
sparse 500k domains
into signals useful for
modeling?

5. The User-Domain Graph
Create features from a User-Domain bipartite graph
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User
s
Domain
s

6. PageRank of Domains
Calculate PageRank using shared users between domains
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7. K-Core of Domains
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Categorize domains by their K-Core

8. Community detection (clustering)
Project to domain-only unipartite graph using ItemSimilarityRecommender,
and detect communities using igraph
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9. Combining Features
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10. Conclusions
• Graph-based approaches offer new opportunities
to extract information for online ad targeting
• Dato’s GraphLab makes graph analytics very easy
• GraphLab a good first step and can be extended
with more specialized libraries
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11. Thank you! Contact me
@n_kowski
kyle@radiumone.com
Learn more
RadiumOne.com
LeanDataScience.com
Work with us
radiumone.com/careers
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Questions?