SlideShare a Scribd company logo
Pinterest
Iterative
supervised
clustering
Adancebetweendata
scienceandmachinelearning
DrJuneAndrews—September2016
ExplorePinterest’scontent
Questionourunderstanding
Inspirethefuture
Agenda
1
2
3
ExplorePinterest’scontent
Questionourunderstanding
Inspirethefuture
Agenda
1
2
3
Clothing
Cooking
Decorating
Beauty
Teaching
Carpentry
Cars
Animated GIFs
Electronics
Stereos
Fashion
Sewing
Articles
Painting
Photography
Nature
Cute cats
Tattoos
Hair
Microscopy
TV shows
Apps
Self help
Motorcycles
Chairs
Fashion
Travel
Garden
Chairs
Food
Linksare

behind

everyPin
Howareusersengaging

withlinkdomains?
2:50 PM 100%
Tool Pros Cons
Cluster algorithms
(SVM, K-Means, Spectral)
• Considers all users
• Accurate
• Tough to communicate
• Definitions change over time
User experience studies • Deep knowledge
• Captures the immeasurable
• Costly
• Considers few users
Domain expert hypothesis • Human interpretable • Inaccurate
Tool Pros Cons
Cluster algorithms
(SVM, K-Means, Spectral)
• Considers all users
• Accurate
• Tough to communicate
• Definitions change over time
User experience studies • Deep knowledge
• Captures the immeasurable
• Costly
• Considers few users
Domain expert hypothesis • Human interpretable • Inaccurate
Currentclusteranalysis
Cleanandloaddataintofavoriteclusteringalgorithm
Buildvisualizationsontopofclusters
Fiddlewithparametersinclusteringalgorithm
Addhumanlabelstoeachcluster
Sharehumaninterpretationofclusters
1
2
3
4
5
Currentclusteranalysis
Cleanandloaddataintofavoriteclusteringalgorithm
Buildvisualizationsontopofclusters
Fiddlewithparametersinclusteringalgorithm
Addhumanlabelstoeachcluster
Sharehumaninterpretationofclusters
1
2
3
4
5
Fatal flaw
Humanintheloopcomputing
Community membership identification from small
seed sets (Kloumann & Kleinberg)
T
Domain Expert
Favorite
Clustering
Algorithm
Humanintheloopcomputing
When machine confidence dips, engage with domain
expert
T
Domain Expert
Favorite
Clustering
Algorithm
?
T
Unsure
Confident
Humanintheloopcomputing
When machine confidence dips, engage with domain
expert
T
Domain Expert
Favorite
Clustering
Algorithm
T
T
Unsure
Confident
?
Humanintheloopcomputing
Domain expert determines when labeling is done
T
Domain Expert
Favorite
Clustering
Algorithm
T
Thats all!
Currentanalysismethodology
Cleanandloaddataintofavoriteclusteringalgorithm
Buildvisualizationsontopofclusters
Fiddlewithparametersinclusteringalgorithm
Addhumanlabelstoeachcluster
Sharehumaninterpretationofclusters
1
2
3
4
5
Humanintheloopcomputing
Stage 1: Machine clusters data
Favorite
Clustering
Algorithm
Humanintheloopcomputing
Stage 2: Domain expert creates 1 human interpretable
cluster
Domain Expert
Humanintheloopcomputing
Stage 3: Remove human labeled clusters and iterate
Favorite
Clustering
Algorithm
Domain Expert
How are users engaging
with link domains?
•Forasamplesetoflinkdomains
we’reinterestedin:
• AllPincreatesintheirfirstyearonPinterest
• AllrepinsintheirfirstyearonPinterest
• 100klinkdomainssampledtotal
Linksarebehind
everyPin
2:50 PM 100%
Python
Notebook
Provides guided iteration
Python
Notebook
Sample visualization 

for each cluster
Python
Notebook
Pin creates Repins
Few Many
Many
Few
Iteration1
Title Dark content
Description Fewer than 2 Pins a week on average
Examples Noisy low quality content
Iteration2
42% of domains left
Few Many Few Some Few Many
0 0 0 0 0 0
Cluster 1 Cluster 3Cluster 2
Pin creates Repins Pin creates RepinsPin creates Repins
Description
Domains with few Pins, but
these Pins thrive in the
Pinterest ecosystem
Calculation
def
detect_pinterest_specials(domain_engagement):
ratio = domain_engagement.n_repins / max(1.0,
float(domain_engagement.n_pin_creates))
return domain_engagement.n_pin_creates <= X
and ratio >= Y
Examples Fashion and impulse sites
Iteration2
Pinterest specials
Few
Pinterest specials
Repins
Many
0 0
Pin creates
Iteration3
33% of domains left
Few Few Few Some Few Many
0 0 0 0 0 0
Cluster 1 Cluster 3Cluster 2
Pin creates Repins Pin creates RepinsPin creates Repins
Iteration3
Steady growth
Description
Active Pin creates and
steady growth throughout
the year
Calculation
def detect_steady_growth(domain_engagement):
(growth_rate, intercept) =
np.polyfit(range(len(domain_engagement.monthly_repins)
), domain_engagement.monthly_repins,1)
return months_pins_created >= X and growth_rate >= Y
Examples Recipe and DIY sites
Some
Steady growth
Repins
Many
0 0
Pin creates
Iteration4
25% of domains left
Few Some Many Some Few Some
0 0 0 0 0 0
Cluster 1 Cluster 3Cluster 2
Pin creates Repins Pin creates RepinsPin creates Repins
Iteration4
Slow growth
Description Similar to steady growth,
but not as fast
Calculation
def detect_steady_growth(domain_engagement):
(growth_rate, intercept) = np.podef
detect_steady_growth(domain_engagement):
(growth_rate, intercept) =
np.polyfit(range(len(domain_engagement.monthly_repins)),
domain_engagement.monthly_repins,1)
return months_pins_created >= X and growth_rate >=
Ylyfit(range(len(domain_engagement.monthly_repins)),
domain_engagement.monthly_repins,1)
return months_pins_created >= X and growth_rate >= Y
Examples Little lower quality recipe 

and DIY sites
Few
Slow growth
Repins
Many
0 0
Pin creates
Iteration5
Churning
Description Slowly fade through the year
Calculation
def detect_churning(domain_engagement):
(repin_growth, intercept) = np.polyfit(
range(len(domain_engagement.monthly_repins) - 2),
domain_engagement.monthly_repins[2:],
1)
(pin_create_growth, intercept) = np.polyfit(
range(len(domain_engagement.monthly_repins) - 2),
domain_engagement.monthly_pin_creates[2:],
1)
return repin_growth < 0 and pin_create_growth < 0
Examples Fashion sale 

and click bait sites
Few
Churning
Repins
Many
0 0
Pin creates
Iteration6
Yearly
Description Slowly fade through the year
Calculation
def detect_churning(domain_engagement):
(repin_growth, intercept) = np.polyfit(
range(len(domain_engagement.monthly_repins) - 2),
domain_engagement.monthly_repins[2:],
1)
(pin_create_growth, intercept) = np.polyfit(
range(len(domain_engagement.monthly_repins) - 2),
domain_engagement.monthly_pin_creates[2:],
1)
return repin_growth < 0 and pin_create_growth < 0
Examples Seasonal fashion, 

such as snow boots
Few
Yearly
Pin creates Repins
Many
0 0
Iteration7
Late bloomer
Description Peak mid year
Calculation
def detect_late_bloomer(domain_engagement):
(concavity, pin_growth, intercept) = np.polyfit(
range(len(domain_engagement.monthly_repins) - 2),
[r + p for (r, p) in zip(domain_engagement.monthly_repins[2:],
domain_engagement.monthly_pin_creates[2:])],
2)
return concavity < 0
Examples Blogs that get off to a slow
start
Few
Pinterest late bloomer
Pin creates Repins
Many
0 0
Clusters
•Darkcontent
•Pinterestspecials
•Steadygrowth
•Slowgrowth
•Churning
•Yearly
•Latebloomer
ExplorePinterest’scontent
Questionourunderstanding
Inspirethefuture
Agenda
1
2
3
Doesasking
twiceyield
thesame
answer?
Shouldweclusteragain?
2:50 PM 100%
Costofreplicatinganalysisis
leavingotherbusiness
opportunitiesonthetable
2:50 PM 100%
Data
scienceis
expensive
Unknown
2:50 PM 100%
Wouldit
makea
difference?
Replication
Crisisin
Psychology
Silberzahn & Ahlmann; Crowdsourced research: Many hands make tight work
NatureAugust2015
Crowd
sourced
studyon
redcards

insoccer
Silberzahn & Ahlmann; Crowdsourced research: Many hands make tight work
NatureOctober2015
TheNewYorkTimesonpredictingthepresidency
September, 2016
Cohn; We Gave Four Good Pollsters the Same Raw Data. They Had Four Different Results.
…butwe’veloweredthecost!
2:50 PM 100%
Data
scienceis
expensive
…9datascientistsand

machinelearningengineers.
Samedata,sameUI,sameday.
Everyonefinishedin~1hour.
…so

wedidit
again
Modelsarealworldsituation
withlimitedresources
9ishuge!
weretheresultsthesame?
Everythingwas
thesame
Baseline
clusters
Results e Results l Results d Results m Results z Results b Results k
Dark content
Pinterest specials
Steady growth
Slow growth
Churning
Yearly
Late bloomer
Existingclustersasourbaseline
Baseline
clusters
Results e Results l Results d Results m Results z Results b Results k
Dark content Unpopular (95%) Trailing (90%)
Pinterest specials Trailing (100%)
Viral on Pinterest
(98%)
Pin creates drop
off (97%)
Steady growth
Increasing repins
(94%)
Continuous
growth (94%)
Slow growth
Churning
Yearly
Late bloomer
90%Matches
Baseline
clusters
Results e Results l Results d Results m Results z Results b Results k
Dark content Unpopular (95%) Trailing (90%)
Original pinny
(84%)
Pinterest specials Trailing (100%)
Minimal original
Pins (66%)
Viral on Pinterest
(98%)
Pin creates drop
off (97%)
Steady growth
Pinterest viral
content (62%) Other (53%)
Original Pinny
(51%)
Viral on the
internet (69%)
Increasing repins
(94%)
Continuous
growth (94%)
Suspected Save
button high Pin
creates (73%)
Slow growth
Pinterest viral
content (55%)
Original Pinny
(82%)
Viral on the
internet (65%)
Increasing repins
(65%)
Continuous
growth (86%)
Suspected Save
button high Pin
creates (51%)
Churning
Original Pinny
(68%)
Viral on the
internet (53%)
Yearly
Original Pinny
(71%)
Late bloomer
Original Pinny
(71%)
Continuous
growth (55%)
Suspected Save
button high Pin
creates (59%)
50%Matches
Baseline
Clusters
Results e Results l Results d Results m Results z Results b Results k
Dark content Unpopular (95%) Trailing (90%)
Original pinny
(84%)
Pinterest specials Trailing (100%)
Minimal original
Pins (66%)
Viral on Pinterest
(98%)
Pin creates drop
off (97%)
Steady growth
Pinterest viral
content (62%) Other (53%)
Original Pinny
(51%)
Viral on the
internet (69%)
Increasing repins
(94%)
Continuous
growth (94%)
Suspected Save
button high Pin
creates (73%)
Slow growth
Pinterest viral
content (55%)
Original Pinny
(82%)
Viral on the
internet (65%)
Increasing repins
(65%)
Continuous
growth (86%)
Suspected Save
button high Pin
creates (51%)
Churning
Original Pinny
(68%)
Viral on the
internet (53%)
Yearly
Original Pinny
(71%)
Late bloomer
Original Pinny
(71%)
Continuous
growth (55%)
Suspected Save
button high Pin
creates (59%)
50%Matches
Baseline
clusters
Results e Results l Results d Results m Results z Results b Results k
Dark content Unpopular (95%) Trailing (90%)
Original pinny
(84%)
Pinterest specials Trailing (100%)
Minimal original
Pins (66%)
Viral on Pinterest
(98%)
Pin creates drop
off (97%)
Steady growth
Pinterest viral
content (62%) Other (53%)
Original Pinny
(51%)
Viral on the
internet (69%)
Increasing repins
(94%)
Continuous
growth (94%)
Suspected Save
button high Pin
creates (73%)
Slow growth
Pinterest viral
content (55%)
Original Pinny
(82%)
Viral on the
internet (65%)
Increasing repins
(65%)
Continuous
growth (86%)
Suspected Save
button high Pin
creates (51%)
Churning
Original Pinny
(68%)
Viral on the
internet (53%)
Yearly
Original Pinny
(71%)
Late bloomer
Original Pinny
(71%)
Continuous
growth (55%)
Suspected Save
button high Pin
creates (59%)
50%Matches
Baseline
clusters
Results e Results l Results d Results m Results z Results b Results k
Dark content Unpopular (95%) Trailing (90%)
Original pinny
(84%)
Pinterest specials Trailing (100%)
Minimal original
Pins (66%)
Viral on Pinterest
(98%)
Pin creates drop
off (97%)
Steady growth
Pinterest viral
content (62%) Other (53%)
Original Pinny
(51%)
Viral on the
internet (69%)
Increasing repins
(94%)
Continuous
growth (94%)
Suspected Save
button high Pin
creates (73%)
Slow growth
Pinterest viral
content (55%)
Original Pinny
(82%)
Viral on the
internet (65%)
Increasing repins
(65%)
Continuous
growth (86%)
Suspected Save
button high Pin
creates (51%)
Churning
Original Pinny
(68%)
Viral on the
internet (53%)
Yearly
Original Pinny
(71%)
Late bloomer
Original Pinny
(71%)
Continuous
growth (55%)
Suspected Save
button high Pin
creates (59%)
50%Matches
Baseline
clusters
Results e Results l Results d Results m Results z Results b Results k
Yearly Seasonal Throwback Seasonal Annual
Steady growth
Gaining
popularity Increasing repins
Continuous
growth High engagement
Pinterest specials Initial flurry
Minimal original
Pins Viral on Pinterest
Pin create drop
off
Unpopular
domains with
good content
Conceptuallysimilarclusters
But not related in implementation
…Goodvs.bad
Differencesinperspective
Two

rootsof
variations
Signsofsuboptimalclustering
•Leadingwithbiases
•Cherry-picking:responding
toalimitedsubsetofthe
data
Few
Seasonal
Pin creates Repins
Few
0 0
Differences
ofperspective
•Resultsm-Viralgrowthcentric
• ViralonPinterest
• Viralontheinternet
• Lame
•Resultsd-Originalcontentcentric
• PersistentoriginalPins
• MinimaloriginalPins
• OriginalPinny
•Resultsl-Returnoninvestmentcentric
• Underserved
• Draught
• Trailing
Impactimplications
9datascientists

9answers
•Productsdependingonclusterused
• Viralmechanisms
• SpeedingPindemotion
• PromotingunderservedPins
•Forsameproduct,

domainsimpacteddifferfor
• Seasonality
• Steadygrowth
• Pinterestspecials
Bottomline
Itmatterswhichdatascientist
doesananalysis
ExplorePinterest’scontent
Questionourunderstanding
Inspirethefuture
Agenda
1
2
3
Let’saskthehardquestion

andbravetheanswertogether
Whenis

datascience

ahouse

ofcards?
Avalancheof
Resources
Measuringdatascienceimpact
•Experimentalsystemsarenowstandard
•Datascientistsaremoreavailable
•Reproducibleanalysis
•[Now]Fastreplicableanalysis
Utilize
Resources
Experiment
• Recordendtoendfromanalysistoimpact
• Innovateonprocesses
• Borrowideasonreplicationfromscience
• Tailorourtechniques forreplication
Concrete
experiments
Breakdowntheproblem

andbuildup
•NarrowDifferenceinPerception
throughPriminganalysts
•Developarubricofexcellence
•Trainanalystsongenerateddata
•Addprocessstabilizers
Pinterest

isinterested
pin.it/Data
Reachout!
DrJuneAndrews
june@pinterest.com/ DrAndrews/ DrJuneAndrews
Let’sdatascience,

datascience!
Let’scrackthecodeto
systematicinnovation
Thankyou!
Wearehiring!
https://engineering.pinterest.com/
pin.it/Data
Replication in Data Science - A Dance Between Data Science & Machine Learning Strata 2016

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