Scalable supervised dimensionality reduction using clustering
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Scalable Supervised Dimensionality Reduction using Clustering. Troy Raeder , Claudia Perlich , Brian Dalessandro , Ori Stitelman , Foster Provost m 6d. What we do. 100 Million Browsers. Who should we target for a product?. Browsing. General browsing. cookies. 100 Million URL’s.

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Scalable Supervised Dimensionality Reduction using Clustering

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Scalable supervised dimensionality reduction using clustering

Scalable Supervised Dimensionality Reduction using Clustering

Troy Raeder, Claudia Perlich, Brian Dalessandro,

OriStitelman, Foster Provost

m6d


What we do

What we do

100 Million

Browsers

Who should

we target for

a product?

Browsing

General browsing

cookies

100 Million

URL’s

Shopping at one of

our campaign sites

Does the ad

have an effect?

conversion

Where should

we advertise and

at what price?

What data should

we pay for?

0.0001% to 1%

baserate

If M6 wins an auction

we serve an ad

Billions of

Auctions

per day

Attribution?

Ad

Exchange


Agnostic data

Agnostic Data

A consumer’s online activity

gets recorded like this:

Purchases

Encoded

date1 3012L20

date 2 4199L30

date n 3075L50

The Branded Web

The Non-Branded Web

Browsing History

Hashed URL’s:

date1 abkcc

date2 kkllo

date3 88iok

date4 7uiol


Our model

Our Model

  • Our goal: To identify people who are likely to purchase a particular product after seeing an ad.

  • Our Approach: A massive, sparse classification problem.

  • Data points: Individual cookies.

    • Features: are past visited URLs.

    • Class: Have you ever bought from Brand X?

  • Our system: Thousands of classification models, with Millions of features per model.


Dimensionality reduction

Dimensionality Reduction

  • Our high-dimensional classification models work really well in most contexts, but in some cases fewer dimensions are better.

  • Rare Events: Some campaigns get very few positives, making it hard to estimate meaningful coefficients.

  • Cold Start: At the very beginning of a campaign, we have seen fewer positive examples. Same problem.

  • Flexibility: There are some things that large models just can’t do (speed).


Dimensionality reduction1

Dimensionality Reduction

  • There are a few obvious options for dimensionality reduction.

  • Hashing: Run each URL through a hash function, and spit out a specified number of buckets.

  • Categorization: We had both free and commercial website category data. Binary URL space  binary category space.www.baseball-reference.com Sports/Baseball/Major_League/Statistics

  • SVD: Singular Value Decomposition in Mahout to transform large, sparse feature space into small dense feature space.

www.dmoz.org


Dimensionality reduction2

Dimensionality Reduction

  • These are all good options, but could we do better?

  • Motivation: Guarantee sufficient representation in the data.

  • Intuition: combine similar URLs together.

  • How should we measure similarity between URLs?

  • Answer: Model parameters!

  • Result: supervised multi-task dimensionality reduction in the space of model parameters.

  • Basic idea: Hierarchical clustering of the URLs themselves.


Setup

Setup

models

U

R

L

S

Table entries are model parameters (Naïve Bayes)


Building the algorithm

Building the Algorithm

  • For hierarchical clustering, we need:

  • A feature space and a distance measure.

    • Pearson correlation in the space of model parameters.

  • A method for cutting the tree.

    • Popularity based.


Example

Example

Home

Kids

Health

Home

News

Games

&

Videos


Experiments

Experiments

  • We built models off data from 28 campaigns.

  • Our production cluster definitions have 4,318 features.

  • We tried to get each of the “challengers” as close to this as we possibly could.

  • We evaluate on Lift (5%) and AUC.


Results

Results


Results1

Results


Results in lab

Results (in lab)


Results in production

Results (in production)


Questions

Questions?

  • Thanks for coming!


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