large-scale real-time product recommendation at criteo

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Copyright © 2015 Criteo Large-Scale Real-Time Product Recommendation at Criteo Simon Dollé RecSys FR, December 1 st , 2015

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Page 1: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

Large-Scale Real-Time Product Recommendation at Criteo

Simon Dollé

RecSys FR, December 1st, 2015

Page 2: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

Page 3: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

We buy

Ad spaces

Page 4: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

We buy

Ad spaces

We sell

Clicks

Page 5: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

We buy

Ad spaces

We sell

Clicksthat convert

Page 6: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

We buy

Ad spaces

We sell

Clicksthat converta lot

Page 7: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

We buy

Ad spaces

We sell

Clicksthat converta lot

We take the risk

Page 8: Large-Scale Real-Time Product Recommendation at Criteo

10 000 displays

Page 9: Large-Scale Real-Time Product Recommendation at Criteo

10 000 displays

leads to

50 clicks

Page 10: Large-Scale Real-Time Product Recommendation at Criteo

10 000 displays

leads to

50 clicks

leads to

1 sale

Page 11: Large-Scale Real-Time Product Recommendation at Criteo

3 billion ads/day3 billion products

Page 12: Large-Scale Real-Time Product Recommendation at Criteo

10ms to pick relevant products

Page 13: Large-Scale Real-Time Product Recommendation at Criteo

7 data centers15 000 servers

1200-node hadoop cluster

Page 14: Large-Scale Real-Time Product Recommendation at Criteo

Catalog data3B+ products

Page 15: Large-Scale Real-Time Product Recommendation at Criteo

Browsing history2B events / day

Catalog data3B+ products

Page 16: Large-Scale Real-Time Product Recommendation at Criteo

Ad display data20B events / day

Browsing history2B events / day

Catalog data3B+ products

Page 17: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

How do we do it ?

Page 18: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

Recommend products for a user

• What we want: reco(user) = products

• 1B users x 3B products !• But we need to scale and keep it fresh

• What we can do :

Pre-select products offline Refine scoring online to get final candidates

Page 19: Large-Scale Real-Time Product Recommendation at Criteo

Bob saw orange shoes

Page 20: Large-Scale Real-Time Product Recommendation at Criteo

Bob saw orange shoes

Some candidate products

Historical

Page 21: Large-Scale Real-Time Product Recommendation at Criteo

Bob saw orange shoes

Some candidate products

Historical

Most viewed

Page 22: Large-Scale Real-Time Product Recommendation at Criteo

Bob saw orange shoes

Some candidate products

Historical

Most viewed

Page 23: Large-Scale Real-Time Product Recommendation at Criteo

Bob saw orange shoes

Some candidate products

Historical

Similar

Most viewed

Page 24: Large-Scale Real-Time Product Recommendation at Criteo

Bob saw orange shoes

Some candidate products

Historical

Similar

Most viewed

Page 25: Large-Scale Real-Time Product Recommendation at Criteo

Bob saw orange shoes

Some candidate products

Historical

Similar

Complementary

Most viewed

Page 26: Large-Scale Real-Time Product Recommendation at Criteo

Recommendation Service20K qps

Page 27: Large-Scale Real-Time Product Recommendation at Criteo

HADOOPBrowsing

history

Recommendation Service

50B

20K qps

Preselection computation Map-Reduce jobs

Page 28: Large-Scale Real-Time Product Recommendation at Criteo

HADOOPBrowsing

history

Preselections

Recommendation Service

50B

12h

20K qps

Preselection computation Map-Reduce jobs

500M

Page 29: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

Online: sources

Similarities Most viewed Most bought

Page 30: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

Online: merge of products

Similarities Most viewed Most bought

Page 31: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

ML model

• Logistic regression models because : • They scale• They are fast• They can handle lots of features

Product-specific User-specific User-product interactions Display-specific

Page 32: Large-Scale Real-Time Product Recommendation at Criteo

HADOOPBrowsing

history

Recommendation Service

50B

12h

20K qps

Preselection computation Map-Reduce jobs

500M

Preselections

Page 33: Large-Scale Real-Time Product Recommendation at Criteo

HADOOPBrowsing

historyPrediction

models

Recommendation Service

50B

12h

6h

20K qps

Preselection computation Map-Reduce jobs

500M

Preselections

Page 34: Large-Scale Real-Time Product Recommendation at Criteo

HADOOPBrowsing

historyPrediction

models

Recommendation Service

50B

12h

6h

20K qps

Display, Click, Sale logs

Preselection computation Map-Reduce jobs

500M

Preselections

Page 35: Large-Scale Real-Time Product Recommendation at Criteo

HADOOPBrowsing

historyPrediction

models

Recommendation Service

50B

12h

6h

20K qps

Display, Click, Sale logs

Preselection computation Map-Reduce jobs

500M

Preselections

Page 36: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

Online: scoring

Similarities Most viewed Most bought

0,02 0,12 0,06 0,18 0,03 0,05 0,01 0,005 0,011 0,013 0,004 0,007

Page 37: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

Online: scoring

Similarities Most viewed Most bought

0,18 0,12 0,06 0,05 0,03 0,02 0,013 0,011 0,01 0,007 0,005 0,004

Page 38: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

Online: candidates

0,18 0,12 0,06 0,05 0,03 0,02 0,013 0,011 0,01 0,007 0,005 0,004

SHOP SHOP SHOP SHOP

-50%

Page 39: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

What’s next ?

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Copyright © 2015 Criteo

What’s next for us: Upcoming challenges

• Long(er)-term user profiles

Page 41: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

What’s next for us: Upcoming challenges

• Long(er)-term user profiles

• More and better product information (images, semantic, NLP)

Page 42: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

What’s next for us: Upcoming challenges

• Long(er)-term user profiles

• More and better product information (images, semantic, NLP)

• Instant-update of similarities

Page 43: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

What’s next for us: Upcoming challenges

• Long(er)-term user profiles

• More and better product information (images, semantic, NLP)

• Instant-update of similarities

• Joint product scoring • (score full banner and not products independently)

Page 44: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

What’s next for you: Fancy a try?

On your own:

With us !http://labs.criteo.com/jobs/

• We published datasets for click prediction• 4GB display-click data: Kaggle challenge in 2014 http://bit.ly/1vgw2XC• 1TB Display-Click data (industry’s largest dataset): http://bit.ly/1PyH4Vq

• 4 billion of observations• 156 billion feature-value• available on Microsoft Azure• used by edX (UC Berkeley)

Page 45: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

Page 46: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

Questions?

Page 47: Large-Scale Real-Time Product Recommendation at Criteo

Copyright © 2015 Criteo

Thank you [email protected]

@simondolle@recsysfr

Credits: Creative Stall, Gilbert Bages