recommender trends 2014
DESCRIPTION
Coming right from the Recommender Systems conference in San Francisco, I present some latest developments in the field of large scale recommendation engines and machine learning.TRANSCRIPT
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Recommender TrendsACM RecSys 2014Silicon Valley USA
Torben Brodtplista GmbH
inspired by ..StammtischNov 13th 2014
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Silicon Valley
Image by New Media at the University of Maine
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RecSys 2014 was ..
● 1 day workshop● 3 day tech conference (see )● 1 day conference
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biased with my experience
● Head of Data Engineering● > 6y plista
○ News, advertising, real-time● Open!
DevOps MathCore
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Contents
1. Product2. Algorithms3. Metrics4. Openness5. Crazy Stuff6. Missing
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Product, ”Data Driven Decisions”
“We take a proposal for an original production or for a piece of content we’re going to buy and we plug in all the data we can abou tit into our models. We’re able to predict reach and hours for that piece of content even before it exists with reasonable precision in a way that helps us to say, ‘this is worth funding’ or ‘that’s not worth funding,’ ”
NEIL HUNT Netflix
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Product, “Search & Recommendationshould (not?) converge”
HECTOR GARCIA-MOLINAProfessor, Stanford University
DEBORA DONATOStumbleUpon
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Product, “Use Human Experts”
ERIC COLSONStitch Fix
Humans send you customized outfits. Machines suggest clothes and judge stuff.
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Product, “Explain your knowledge”
● Xbox explains why their recommendations are utile
● Cortana builds ML model of user and still allows to change it
Build Trust!
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Product, “Care about Privacy”
once you lose your customer because of privacy, you will never get him back
solutions● store user history on client side● ..
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Product, ”Allow User Interaction”
HECTOR GARCIA-MOLINAProfessor, Computer Science and Electrical Engineering Departments of Stanford University
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Product, “active learning”
Why do vague passive learning when you can ask the user?
.. implicitly or explicitly
http://en.wikipedia.org/wiki/Active_learning_(machine_learning)
SMRITI BHAGATTechnicolor
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Algorithms
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Algorithms, ”Matrix Factorization”
[...] faster by replacing inner product with PCA trees
NOAM KOENIGSTEINMicrosoft R&D
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Algorithms, “Ensembles”
● Multi Armed Bandits● Ensemble Methods● Global Optimization
https://github.com/Yelp/MOE
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Algorithms, “How does MOE work”
DR. SCOTT CLARKYelp
1. Build Gaussian Process (GP) with points sampled so far
2. Optimize covariance hyperparameters of GP3. Find point(s) of highest Expected Improvement
within parameter domain4. Return optimal next best point(s) to sample
https://github.com/Yelp/MOE
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Algorithms, “Topic Modelling”
● LDA is standard● datascience tasks
○ where to cut○ how many topics
● where to use?
http://en.wikipedia.org/wiki/Topic_model
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Algorithms, “Content”
● Sense identifiers (int) instead of keywords● Word sense disambiguation
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Metrics, “Stakeholders”
● Business Value● Consumer Value● Conflicting goals?● Diversity?
NEIL HUNT Netflix
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Metrics, “Dwell Time”
● Client Side implementation
● Yahoo ensures dwell-time is comparable across different context (device, etc)
● it correlates to clicks, but is more meaningful XING YI
Yahoo Labs
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Metrics, “Increasing signals”
Get the full lifetime journey● reservation● rating● billing / tipping
JEREMY SCHIFFOpenTable
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Openness, “Software Side”
Companies share software● credits to Twitter, Yelp, others
Finally Paper results can be reused (github)
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Openness, “Data Side”
Wikipedia, DBPedia, common crawl
Companies share Data & Challenges● credits to Netflix, Tmall, Criteo
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Openness, “Connectivity”
Everything is possible!To Me and to You
● Connect to Facebook○ access open graph
● Get Fulltext without 10k servers● Use Apache Mahout, Azure ML, etc
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Openness, “Connectivity”
● Give students the chance to learn
● CoLaboratory Notebook
http://venturebeat.com/2014/08/08/google-whips-up-a-chrome-app-to-let-data-scientists-work-together/
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Openness, “Connectivity”
● Azure Marketplace allows to exchange machine learning models
● RapidMiner makes workflows reproducable
https://datamarket.azure.com/browse/data
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Crazy Stuff
Industry Sessions…● Facebook News● Shopkick● Stumble Upon● climate institute● ...
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Crazy Stuff, “music genome project”
1 song = 450 musical characteristics from trained music analyst
ERIK M. SCHMIDTPandora
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Crazy Stuff, “LinkedIn A/B testing”
● XLNT Platform● Key Component !● Continuous Deployment
YA XULinkedInhttps://engineering.linkedin.com/ab-testing/xlnt-
platform-driving-ab-testing-linkedin
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Crazy Stuff, “Google Deep Learning”
● Application?○ Pixels, Audio, Searches,
Translation● Embeddings● Language Models● Scalability
JEFF DEANGoogle
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Missing? “Uncovered Topics”
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Missing, “Probabilistic Data Structures”
probabilistic counting, hyperLogLog, etc
http://research.neustar.biz/https://streamdrill.com/
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Missing
Large Scale?● Computational Costs● Real-Time Recs
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Questions?
Torben Brodtplista GmbH
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● hard to convince mgmt (?!)● start measuring
example● coupons 1/week might
decrease revenueJEREMY SCHIFFOpenTable
Metrics, “Long Term Satisfaction”
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Resume, ”we enhance services”
Large Size Companies cannot exist without data science● Netflix● Zalando● etc