Graph-based Feature Extraction for Online Advertising Prediction

Kyle NapierkowskiRadiumOne

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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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Online Advertising Prediction

Browsing Visit Advertiser Site Buy Product

IN A NUTSHELL: Predict a user’s interest in buying products based on browsing behavior

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The User-Domain MatrixRepresent m users’ visits to n web domains as a matrix

a.com

b.com

… 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?

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The User-Domain GraphCreate features from a User-Domain bipartite graph

Users

Domains

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PageRank of DomainsCalculate PageRank using shared users between domains

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K-Core of DomainsCategorize domains by their K-Core

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Community detection (clustering)Project to domain-only unipartite graph using ItemSimilarityRecommender,

and detect communities using igraph

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Combining Features

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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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Thank you! Contact me@n_kowskikyle@radiumone.com

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Questions?