Recommender Trends 2014

@torbenbrodt #recsys
Recommender Trends
ACM RecSys 2014
Silicon Valley USA
Torben Brodt
plista GmbH
inspired by ..Stammtisch
Nov 13th 2014

Silicon Valley
Image by New Media at the University of Maine

RecSys 2014 was ..
● 1 day workshop
● 3 day tech conference (see )
● 1 day conference

biased with my experience
● Head of Data Engineering
● > 6y plista
○ News, advertising, real-time
● Open!
DevOps Math
Core

Contents
1. Product
2. Algorithms
3. Metrics
4. Openness
5. Crazy Stuff
6. Missing

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

Product, “Search & Recommendation
should (not?) converge”
HECTOR GARCIA-MOLINA
Professor, Stanford University
DEBORA DONATO
StumbleUpon

Product, “Use Human Experts”
ERIC COLSON
Stitch Fix
Humans send you customized
outfits. Machines suggest clothes
and judge stuff.

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!

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
● ..

Product, ”Allow User Interaction”
HECTOR GARCIA-MOLINA
Professor, Computer Science and Electrical Engineering
Departments of Stanford University

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 BHAGAT
Technicolor

Algorithms

Algorithms, ”Matrix Factorization”
[...] faster by replacing inner product with
PCA trees
NOAM KOENIGSTEIN
Microsoft R&D

Algorithms, “Ensembles”
● Multi Armed Bandits
● Ensemble Methods
● Global Optimization
https://github.com/Yelp/MOE

Algorithms, “How does MOE work”
DR. SCOTT CLARK
Yelp
1. Build Gaussian Process (GP) with points
sampled so far
2. Optimize covariance hyperparameters of GP
3. Find point(s) of highest Expected Improvement
within parameter domain
4. Return optimal next best point(s) to sample
https://github.com/Yelp/MOE

Algorithms, “Topic Modelling”
● LDA is standard
● datascience tasks
○ where to cut
○ how many topics
● where to use?
http://en.wikipedia.org/wiki/Topic_model

Algorithms, “Content”
● Sense identifiers (int) instead of keywords
● Word sense disambiguation

Metrics, “Stakeholders”
● Business Value
● Consumer Value
● Conflicting goals?
● Diversity?
NEIL HUNT Netflix

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

Metrics, “Increasing signals”
Get the full lifetime journey
● reservation
● rating
● billing / tipping
JEREMY SCHIFF
OpenTable

Openness, “Software Side”
Companies share software
● credits to Twitter, Yelp, others
Finally Paper results can be reused (github)

Openness, “Data Side”
Wikipedia, DBPedia, common crawl
Companies share Data & Challenges
● credits to Netflix, Tmall, Criteo

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

● 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/

● Azure Marketplace
allows to exchange
machine learning models
● RapidMiner makes
workflows reproducable
https://datamarket.azure.com/browse/data

Crazy Stuff
Industry Sessions…
● Facebook News
● Shopkick
● Stumble Upon
● climate institute
● ...

Crazy Stuff, “music genome project”
1 song = 450 musical
characteristics from
trained music analyst
ERIK M. SCHMIDT
Pandora

Crazy Stuff, “LinkedIn A/B testing”
● XLNT Platform
● Key Component !
● Continuous Deployment
YA XU
LinkedIn
https://engineering.linkedin.com/ab-testing/xlnt-
platform-driving-ab-testing-linkedin

Crazy Stuff, “Google Deep Learning”
● Application?
○ Pixels, Audio, Searches,
Translation
● Embeddings
● Language Models
● Scalability
JEFF DEAN
Google

Missing? “Uncovered Topics”

Missing, “Probabilistic Data Structures”
probabilistic counting, hyperLogLog, etc
http://research.neustar.biz/
https://streamdrill.com/

Missing
Large Scale?
● Computational Costs
● Real-Time Recs

Questions?
Torben Brodt
plista GmbH

● hard to convince mgmt (?!)
● start measuring
example
● coupons 1/week might
decrease revenue
JEREMY SCHIFF
OpenTable
Metrics, “Long Term Satisfaction”

Resume, ”we enhance services”
Large Size Companies cannot exist without
data science
● Netflix
● Zalando
● etc

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