Interactive Topic Graph Extraction and Exploration of Web Content
Graph-based Feature Extraction for Online Advertising Targeting
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Transcript of Graph-based Feature Extraction for Online Advertising Targeting
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@[email protected]
Learn moreRadiumOne.comLeanDataScience.com
Work with usradiumone.com/careers
Questions?