Data and Personalization: The Marketplace Holy Grail !

Kamelia Aryafar (@KAryafar) , Data Scientist @ Etsy !

https://github.com/karyafar/Extract2015

D a t a S c i e n c e

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Kamelia Aryafar

Diane Hu Andrew Clegg

Rob Hall Josh Attenberg

Corey Lynch Caitlin Cellier

Etsy is an online marketplace where people connect to buy and sell unique goods: Handmade, vintage, or craft supplies

E t s y O v e r v i e w

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ColoredStarsRome , Italy

E t s y O v e r v i e w

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~21M Active Buyers

1.4M Active Sellers

~35M Listings

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Stats as of March 31, 2015

People buy and sell on Etsy from nearly every country in the world.

In a 2014 survey of Etsy buyers, 92% agreed that Etsy offers products they cannot find elsewhere.

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Unique Items, Unique Challenges

PedantWorld

8Circuit Board Pendant!

E t s y O v e r v i e w

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E t s y O v e r v i e w

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Favorite an item or shop, and

add to collections with coherent theme/style

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Follow another user with similar

style/interest !

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Browse(Unintentional)

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Search (Intentional)

E t s y O v e r v i e w

How do people typically use Etsy?

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Purchase an item

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Marketing Emails Including Recommendations

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Pinterest Facebook

Search Engines !

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Data, Data, Data

R e c o m m e n d a t i o n s o n E t s y

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ITEM RECS USER REC SHOP REC

R e c o m m e n d a t i o n s o n E t s y

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R e c o m m e n d a t i o n s o n E t s y

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M a r k e t i n g E m a i l s

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C a r t R e c o m m e n d a t i o n s

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E m p t y C a r t R e c o m m e n d a t i o n s

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Personalization

• Collect Data

• Scrub

• Model

• Generate Recommendations

Pe r sona l i za t ion

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M a t r i x Fa c t o r i z a t i o n ( M F )

21Slide Credit: Rob Hall

M a t r i x Fa c t o r i z a t i o n ( M F )

22Slide Credit: Rob Hall

M a t r i x Fa c t o r i z a t i o n ( M F )

23Slide Credit: Rob Hall

L o c a l i t y S e n s i t i v e H a s h i n g ( L S H )

24Slide Credit: Rob Hall

L a t e n t D i r i c h l e t A l l o c a t i o n ( L D A )

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Discover popular styles on Etsy as a distribution over items

Represent each user as a distribution over popular styles, i.e. “style profile”

. . .

V

K

“geometric” “mid-century” “surreal”

. . . K

0.38

0.13

0.02

. . .

“geometric”

“mid-century”

“surreal”=

Slide Credit: Diane Hu

D i s c o v e r i n g U s e r S t y l e s

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A N I M A L S

G E O M E T R I C

M I D - C E N T U RY M O D E R N

T E N TA C L E S

Different styles discovered by LDA

Slide Credit: Diane Hu

G e n e r a t i n g R e c o m m e n d a t i o n s

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S T Y L E # 4 2 8

S T Y L E # 6 5 5

S T Y L E # 5 4

S T Y L E # 8 7

U S E R ’ S FAV O R I T E S

I T E M R E C O M M E N D AT I O N S

Slide Credit: Diane Hu

What We Have Learned

I m p a c t

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A/B Testing

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•Variant: LDA or MF personalized recommendations •Control : No recommendations

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• Significant increase in all business metrics

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Personalized content best engages users and enhances business metrics.

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More Data, More accurate models, Better recommendations

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Thank you!

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Q u e s t i o n s ?

https://github.com/karyafar/Extract2015