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H U M B E RTO C O R O N A
@ TO TO PA M P I N
F I N D I N G T H E R I G H T FA S H I O N
F O R E A C H I N D I V I D U A L
R E C O M M E N D E R S Y S T E M S
T H R O U G H FA S H I O N
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TA B L E O F C O N T E N T S
about Zalando
what and why on recommendations
curated recommendations
content-based recommendations
collaborative recommendations
context is Queen!
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A B O U T Z A L A N D O
~ 60% MOBILE VISITS
135 M MONTHLY
VISITS
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https://corporate.zalando.com/en/newsroom
A B O U T Z A L A N D O
3B REVENUE
18 MILLION ACTIVE
CUSTOMERS1K
TECHIES
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I N E E D A PA I R O F T R A I N E R S
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I N E E D A PA I R O F T R A I N E R S
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M E N T R A I N E R S
3 1 8 4
W O M E N T R A I N E R S
2 6 6 7
K I D S T R A I N E R S
1 4 0 3
I D O N ’ T N E E D A PA I R O F T R A I N E R S I N E E D T H E 😍 PA I R O F T R A I N E R S
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S O M E T R A I N E R S I L I K E …
R E C O M M E N D AT I O N S A R E I M P O R TA N T
? ¿
B A R G A I NH U N T
I N S P I R E C O L L E C T
S E A R C H
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SERENDIPITY
RELEVANCE
CONTEXT
TRANSPARENCY
REACH
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If you like the beatles you might like...: a tutorial on music recommendation (MM'08)
R E C O M M E N D AT I O N S A R E I M P O R TA N T
human-curated recommendations
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M O N O C R H O M E G R AY S C A L E C O M F Y S H O E S
A D I D A SN I K E VA N S
T R E N D Y S H O E S C L A S S I C S H A P E S
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Z A L O N H U M A N - C U R AT E D R E C O M M E N D AT I O N S
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machine recommendations: content based
recommendations
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D E S C R I B I N G T H E I T E M S
CATEGORY: SHOES – TRAINERS BRAND: NIKE, ADIDAS
SIZE: 40.5 (NOT 40, NOT 41) COLOR: GRAY , BLACK, WHITE, MONOCHROME
PATTERN: POLKA DOT UPPER MATERIAL: TEXTILE / SYNTHETICS
INTERNAL MATERIAL: TEXTILE SHOE FASTENER: LACES
SEASON: SS2016 TRENDS: URBAN, FASHION X SPORTS
CATEGORY: SHOES – TRAINERS
BRAND: NIKE, ADIDAS, VANS, PUMA SIZE: 40.5, 41
COLOR: GRAY , BLACK, GREEN, RED, SILVER, PATTERN: NONE
UPPER MATERIAL: TEXTILE / SYNTHETICS, LEATHER INTERNAL MATERIAL: TEXTILE, VEGAN LEATHER
SHOE FASTENER: LACES SEASON: SS2016, SS2015, AW2015
TRENDS: URBAN, MINIMALISM, SKATE
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D E S C R I B I N G T H E C U S TO M E R S
M AT C H I N G P E O P L E W I T H FA S H I O N C O N T E N T- B A S E D R E C O M M E N D AT I O N S
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C AT E G O RY
B R A N D
S I Z E
C O L O R
PAT T E R N
U P P E R M AT E R I A L
I N T E R N A L M AT E R I A L
S H O E FA S T E N E R S
S E A S O N
T R E N D S
PROS OF CBF • No need for explicit user feedback
• Can recommend items as soon as they are introduced in the shop
• Very transparent and allows for explanations
• Good for finding
CONS OF CBF • Need for rich metadata
• Not diverse or novel recommendations
• Not great for discovery
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M AT C H I N G P E O P L E W I T H FA S H I O N C O N T E N T- B A S E D R E C O M M E N D AT I O N S
machine recommendations: collaborative
recommendations
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https://www.zalando.co.uk/women-street-style/https://www.zalando.co.uk/men-street-style/
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Anna 1 … 1 3 1Justin … 4 2
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.
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... ...
……......
... ...
... ...
April 5 … 10 2Ellen 1 1 … 2Javier 1 … 1
D ATA R E P R E S E N TAT I O N
Anna 1 … 1 3 1Justin " … " 4 2
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.
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... ...
……......
... ...
... ...
April 5 … 10 2Ellen 1 1 … 2Javier 1 … 1
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F I N D I N G C U S TO M E R S S I M I L A R TO Y O U
Anna 1 … 1 3 1Justin … 4 2
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.
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... ...
……......
... ...
... ...
April 5 … 10 2Ellen 1 1 … 2 #
Javier 1 … 125
F I N D I N G I T E M S S I M I L A R TO T H E O N E S Y O U L I K E
C O L L A B O R AT I V E R E C O M M E N D AT I O N S
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PROS: • Very accurate recommendations
• Diverse recommendations
• Good for discovery and inspiration
CONS: • Harry Potter effect
• Lonely Wolf effect
• Cold-start problem (for products and customers)
context
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context is queen
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C O N T E X T: M O B I L E O R D E S K TO P ?
C O N T E X T - S E A S O N , W E AT H E R , L O C AT I O N
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R E C O M M E N D AT I O N S & P E R S O N A L I S AT I O N
A D S
M A I L
F E E D
C U S TO M E R S
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R E C O M M E N D AT I O N A N D P E R S O N A L I S AT I O N , O T H E R A P P L I C AT I O N S
S TO C K
@totopampin
hcorona.github.io zalando.github.io
