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Combining Statistics and
Expert Human Judgment for
Better Recommendations
AIRBNB LUNCH LEARNING | MARCH 2016
Jay Wang Linkedin | Twitter @qqwjq
About me
Innovations in four basic elements of everyday life
TransportationLodging
How Stitch Fix works
How Stitch Fix works
How Stitch Fix works
How Stitch Fix works
How Stitch Fix works
The life of a fix
Making a bet on every shipment
• Shipping both ways
• Disappointing client
experience
• Inventory opportunity cost
• Convenience
• Discovery
The life of a fix
Recommendations
Human curation Fulfillment
Algorithmic recommendations followed by human curation
The life of a fix
Recommendations
Human curation Fulfillment
Algorithmic recommendations followed by human curation
Styling at Stitch Fix
?
Learn from data!
Making predictions – merchandise data
Neck
HipLength
Shoulder cut
Fabrication
Hem pattern
Top cut
• Climate
• Body type affinities
• Opacity
• …
Waistline
style
Draping
style
Making predictions – client data
Mom: True
Profession: Finance
Location: Nashville
Age: 31
Weight:
135
Height:
5’4”
Favorite
colors:
blue, black,
tan
Cup
size: B
Torso:Forearm :: 2:1
Incentive to provide accurate data
Predicting success – client feedback
Shipment survey
Reason for non-purchase Biases in purchase data
Predicting success – putting it all together
Predicting success – putting it all together
preppy
edgy
glam
boho
casual
classic
romantic
business
Predicting success – putting it all together
preppy
edgy
glam
boho
casual
classic
romantic
business
Predicting success – putting it all together
preppy
edgy
glam
boho
casual
classic
romantic
business
Predicting success – putting it all together
preppy
edgy
glam
boho
casual
classic
romantic
business
LDA Factorization machineLearn to rank
Predicting success – putting it all together
f (•)
The life of a fix
Recommendations Human curation Fulfillment
Everyone’s personal stylist
Not sales people
Humans excel at unstructured data
Humans expand the scope
of data and interactions
we can consider
Humans excel at unstructured data
I am going on a cruise next
month – send me some
dresses for sunny weather!
Humans expand the scope
of data and interactions
we can consider
Humans excel at unstructured data
I am going on a cruise next
month – send me some
dresses for sunny weather!
Humans expand the scope
of data and interactions
we can consider
Humans excel at unstructured data
I am going on a cruise next
month – send me some
dresses for sunny weather!
Humans expand the scope
of data and interactions
we can consider
Encouraging rapid iteration
!• The algorithm developer is freed from
optimizing edge cases
• This lowers the bar for new
algorithms and encourages rapid
iteration
Human selection censors data
Example: Some clients prefer not
flaunting their arms
Stylist will correctly avoid
sending items that flaunt arms
to these clients
Human selection is great for the client – but complicated for the data
scientist!
Human selection censors data
Example: Some clients prefer not
flaunting their arms
Stylist will correctly avoid
sending items that flaunt arms
to these clients
Result: We won’t have data for violating
flaunt preferences!
Human selection is great for the client – but complicated for the
statistician!
Human selection adds bias
Example: It is summer in Texas
Stylist will correctly avoid sending
heavy winter clothes. We will only
send it when there is a good
reason
Human selection adds bias
Example: It is summer in Texas
Client request:
I’m going to Antarctica!
Stylist will correctly avoid sending
heavy winter clothes. We will only
send it when there is a good
reason
Human selection adds bias
Example: It is summer in Texas
Client request:
I’m going to Antarctica!
Incorrect conclusion:
People in Texas love this winter jacket!
Stylist will correctly avoid sending
heavy winter clothes. We will only
send it when there is a good
reason
Selection bias
Success data is biased by the stylist selection
Subject to selection effect
• Simpson’s paradox directly comparing style
success rate
• Style A success rate 70% vs style B 60%
• Things algo can’t learn : tank top on arm flaunt
clients?
?
Predicting selection
What about predicting selection?
Simple but selection is not really success
Creates a direct feedback loop
Combining selection and success models
You should probably consider both
We should aim to recommend items that
• Stylists will want to send (credibility)
• Will be successful with the client when selected
(success)
Disagreement between selection & success?
Selection
model
Success
model
vs
Disagreement between selection and success -- good
Ignoring an inappropriate recommendation
Client request: “I need an outfit for a glamorous night out!”
Disagreement between selection and success -- good
Ignoring an inappropriate recommendation
Client request: “I need an outfit for a glamorous night out!”
Disagreement between selection and success -- bad
Stylist not choosing something that would be
successful
Predicted
probability
of success = 85%
?
?
Disagreement between selection and success -- bad
Stylist not choosing something that would be successful
Could lack trust in the recommendation : importance of
transparency
Predicted
probability
of success = 85%
?
?
Based on
her
recent
purchase
Machine – human collaboration effective but brings its own challenge
• Excel at unstructured data
• Enduring relationships
• Selection censors the data
• Make the problem even more
interesting!
?
Humans
• How do stylists make selections?
• How do human & machine collaborate?
Challenges introduced by humans in the loop
Innovation + business verticals necessitate a large team
• client algorithms
• Signup profile
• Churn/LTV model
• Demand forecasting
• merch algorithms
• Planning and allocation
• Dynamics of inventory
• Clearance
• data labs
• NLP
• NN
• Computer vision
From “highly scalable blog”
Innovation + business verticals necessitate a large team
• As you may have noticed, we
have a rather large team
• Our team size required by
having many different business
verticals
• Because it’s a green field, new
vertical ! Algo is actively
growing
Wealth of problems
Questions?
Service? Data science team?

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Artificial Intelligence in fashion -- Combining Statistics and Expert Human Judgment for Better Recommendations

  • 1. Combining Statistics and Expert Human Judgment for Better Recommendations AIRBNB LUNCH LEARNING | MARCH 2016 Jay Wang Linkedin | Twitter @qqwjq
  • 3. Innovations in four basic elements of everyday life TransportationLodging
  • 9. The life of a fix Making a bet on every shipment • Shipping both ways • Disappointing client experience • Inventory opportunity cost • Convenience • Discovery
  • 10. The life of a fix Recommendations Human curation Fulfillment Algorithmic recommendations followed by human curation
  • 11. The life of a fix Recommendations Human curation Fulfillment Algorithmic recommendations followed by human curation
  • 12. Styling at Stitch Fix ? Learn from data!
  • 13. Making predictions – merchandise data Neck HipLength Shoulder cut Fabrication Hem pattern Top cut • Climate • Body type affinities • Opacity • … Waistline style Draping style
  • 14. Making predictions – client data Mom: True Profession: Finance Location: Nashville Age: 31 Weight: 135 Height: 5’4” Favorite colors: blue, black, tan Cup size: B Torso:Forearm :: 2:1 Incentive to provide accurate data
  • 15. Predicting success – client feedback Shipment survey Reason for non-purchase Biases in purchase data
  • 16. Predicting success – putting it all together
  • 17. Predicting success – putting it all together preppy edgy glam boho casual classic romantic business
  • 18. Predicting success – putting it all together preppy edgy glam boho casual classic romantic business
  • 19. Predicting success – putting it all together preppy edgy glam boho casual classic romantic business
  • 20. Predicting success – putting it all together preppy edgy glam boho casual classic romantic business LDA Factorization machineLearn to rank
  • 21. Predicting success – putting it all together f (•)
  • 22. The life of a fix Recommendations Human curation Fulfillment Everyone’s personal stylist Not sales people
  • 23. Humans excel at unstructured data Humans expand the scope of data and interactions we can consider
  • 24. Humans excel at unstructured data I am going on a cruise next month – send me some dresses for sunny weather! Humans expand the scope of data and interactions we can consider
  • 25. Humans excel at unstructured data I am going on a cruise next month – send me some dresses for sunny weather! Humans expand the scope of data and interactions we can consider
  • 26. Humans excel at unstructured data I am going on a cruise next month – send me some dresses for sunny weather! Humans expand the scope of data and interactions we can consider
  • 27. Encouraging rapid iteration !• The algorithm developer is freed from optimizing edge cases • This lowers the bar for new algorithms and encourages rapid iteration
  • 28. Human selection censors data Example: Some clients prefer not flaunting their arms Stylist will correctly avoid sending items that flaunt arms to these clients Human selection is great for the client – but complicated for the data scientist!
  • 29. Human selection censors data Example: Some clients prefer not flaunting their arms Stylist will correctly avoid sending items that flaunt arms to these clients Result: We won’t have data for violating flaunt preferences! Human selection is great for the client – but complicated for the statistician!
  • 30. Human selection adds bias Example: It is summer in Texas Stylist will correctly avoid sending heavy winter clothes. We will only send it when there is a good reason
  • 31. Human selection adds bias Example: It is summer in Texas Client request: I’m going to Antarctica! Stylist will correctly avoid sending heavy winter clothes. We will only send it when there is a good reason
  • 32. Human selection adds bias Example: It is summer in Texas Client request: I’m going to Antarctica! Incorrect conclusion: People in Texas love this winter jacket! Stylist will correctly avoid sending heavy winter clothes. We will only send it when there is a good reason
  • 33. Selection bias Success data is biased by the stylist selection Subject to selection effect • Simpson’s paradox directly comparing style success rate • Style A success rate 70% vs style B 60% • Things algo can’t learn : tank top on arm flaunt clients? ?
  • 34. Predicting selection What about predicting selection? Simple but selection is not really success Creates a direct feedback loop
  • 35. Combining selection and success models You should probably consider both We should aim to recommend items that • Stylists will want to send (credibility) • Will be successful with the client when selected (success) Disagreement between selection & success? Selection model Success model vs
  • 36. Disagreement between selection and success -- good Ignoring an inappropriate recommendation Client request: “I need an outfit for a glamorous night out!”
  • 37. Disagreement between selection and success -- good Ignoring an inappropriate recommendation Client request: “I need an outfit for a glamorous night out!”
  • 38. Disagreement between selection and success -- bad Stylist not choosing something that would be successful Predicted probability of success = 85% ? ?
  • 39. Disagreement between selection and success -- bad Stylist not choosing something that would be successful Could lack trust in the recommendation : importance of transparency Predicted probability of success = 85% ? ? Based on her recent purchase
  • 40. Machine – human collaboration effective but brings its own challenge • Excel at unstructured data • Enduring relationships • Selection censors the data • Make the problem even more interesting! ? Humans • How do stylists make selections? • How do human & machine collaborate? Challenges introduced by humans in the loop
  • 41. Innovation + business verticals necessitate a large team • client algorithms • Signup profile • Churn/LTV model • Demand forecasting • merch algorithms • Planning and allocation • Dynamics of inventory • Clearance • data labs • NLP • NN • Computer vision From “highly scalable blog”
  • 42. Innovation + business verticals necessitate a large team • As you may have noticed, we have a rather large team • Our team size required by having many different business verticals • Because it’s a green field, new vertical ! Algo is actively growing Wealth of problems