SlideShare a Scribd company logo
Improving search experience with a
taxonomy in the fashion domain
George Cushen & Pedro Balage
Taxonomy Boot Camp 2019
Farfetch at a glance
2
> 3,000*
Employees across 13 countries
$1.4 Billion*
Gross Merchandise Value
> 3,000*
Brands available for consumers
to shop
> 1,000**
Luxury sellers on the
Marketplace
$601**
AOV on Marketplace
> 2.9 Million*
Orders on Marketplace
1.7 million**
Active Marketplace consumers
$307 Billion
Size of personal luxury good
industry (Bain estimates)
*Correct for full year 2018 **As at Q1 2019
15**
Marketplace language sites
3
Building a Taxonomy for Fashion
4
Outfit available from
https://www.farfetch.com
📖 Standardising with a unified semantic fashion taxonomy
🏷 Connecting these fashion entities with products in a graph via AI
🔍 Applying the taxonomy and graph to improve the search experience
We’re mapping fashion DNA to decode personal style
Data Science Data Science
Taxonomy
Knowledge
Graph
Categorisation
6
Where Business Meets Fashion
A domain specific knowledge graph for fashion.
Business vs Fashion Entities
Business Fashion
Product
Content
Brand
Category
Customer
Season
Gender
...
Occasion
Celebration
Theme
Style
Trend
DNA
Pattern
Colour
Material
Synonym
...
Order
Payment
Promotion
Review
...
How many ways can you say “puffer jacket”?
Padded
coat
Down
coat
Duvet
coat
Quilted
coat
Puffer
jacket
Down-filled
jacket
Down
jacket
Quilted
jacket
Duvet
jacket
Down-filled
coat
Padded
jacket
Puffer
coat
9
Perception
10
Subjectivity
Skinny
11
Universal Fashion Taxonomy
Fashion
Taxonomy
Synonyms
Descriptive
attributes
Brand DNA
Materials
ColoursTrends
Editorial,
emotive,
seasonal
concepts
Textile Cotton Denim
Product
2
Swedish
Design
Acne
Taxonomy
Bootcamp
Conferen
ce
Autumn
Product
1
PrintsTree
Blue
Light
Blue
12
Associating entities in a Knowledge Graph
13
What is a knowledge graph?
A knowledge graph can describe
● a collection of nodes (entities) representing business and fashion entities
has_term
● and with labeled relationships between the nodes
Product
D&G
tote bag
Attribute
Leopard
Print Attribute
Leopard
Spots
Attribute
Animal
Print
Properties:
Language = “EN”
● each containing information (properties)
Properties:
ProductID = 123
14
Dots and Lines
15
AI: A Multi-Modal Multi-Task Approach
Images Text
Computer
Vision NLP
Deep
Classifier
Example output
Product Type: Dress
Colour: White
Occasion: Wedding
Theme: Classic
16
Building a fashion knowledge graph
Search Recommendations ...
Fashion Knowledge Graph
Associates fashion entities with business entities
AI Knowledge cleaning Entity resolution Schema mapping
Applications
Taxonomy &
Graph
Construction
Knowledge
Collection
Expert Knowledge Data-Driven Insights
17
What did we learn?
✅ Collaborate early with SMEs
✅ Incorporate data-driven insights
✅ Invest in defining a unified taxonomy/graph and
share it
✅ Consider perspective: seller vs consumer
facing metadata
✅ Use and promote standards where possible
18
Application to Search
Use of Taxonomies in Search
● They help us to understand the domain vocabulary and its structure
19
How modern architectures of Search work
20
● Autocomplete
● Query Understanding
● Query Expansion
● Relevance Re-scorer
● Ranking
QueryAutocomplete
Query
Understanding
Query Expansion
Relevance Re-scorer
Ranking
Products
Taxonomy
Query Expansion
21
Products
Products
Products
Products
Products
How to mix different set of products?
22
Products
Query A Query B Query C Query D
Products Products Products
Products
Relevance Re-scorer
How to score different expansions?
23
By = λ1 + λ2 C+ λ3 D+ λ4A
● Each expansion gives a score for each product
● Combined score is a is a parameterized model
● Parameters may be trained using click logs and machine learning
● The threshold cut could also be learned
24
Challenges ahead
● Internationalization of the taxonomy for other languages
○ Can a knowledge graph help with that?
25
26
Questions
@GeorgeCushen
@PedroBalage
#Farfetch
We’re hiring!

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Improving search experience with a taxonomy in the fashion domain

  • 1. Improving search experience with a taxonomy in the fashion domain George Cushen & Pedro Balage Taxonomy Boot Camp 2019
  • 2. Farfetch at a glance 2 > 3,000* Employees across 13 countries $1.4 Billion* Gross Merchandise Value > 3,000* Brands available for consumers to shop > 1,000** Luxury sellers on the Marketplace $601** AOV on Marketplace > 2.9 Million* Orders on Marketplace 1.7 million** Active Marketplace consumers $307 Billion Size of personal luxury good industry (Bain estimates) *Correct for full year 2018 **As at Q1 2019 15** Marketplace language sites
  • 3. 3 Building a Taxonomy for Fashion
  • 5. 📖 Standardising with a unified semantic fashion taxonomy 🏷 Connecting these fashion entities with products in a graph via AI 🔍 Applying the taxonomy and graph to improve the search experience We’re mapping fashion DNA to decode personal style Data Science Data Science Taxonomy Knowledge Graph
  • 7. Where Business Meets Fashion A domain specific knowledge graph for fashion. Business vs Fashion Entities Business Fashion Product Content Brand Category Customer Season Gender ... Occasion Celebration Theme Style Trend DNA Pattern Colour Material Synonym ... Order Payment Promotion Review ...
  • 8. How many ways can you say “puffer jacket”? Padded coat Down coat Duvet coat Quilted coat Puffer jacket Down-filled jacket Down jacket Quilted jacket Duvet jacket Down-filled coat Padded jacket Puffer coat
  • 11. Skinny 11 Universal Fashion Taxonomy Fashion Taxonomy Synonyms Descriptive attributes Brand DNA Materials ColoursTrends Editorial, emotive, seasonal concepts Textile Cotton Denim Product 2 Swedish Design Acne Taxonomy Bootcamp Conferen ce Autumn Product 1 PrintsTree Blue Light Blue
  • 12. 12 Associating entities in a Knowledge Graph
  • 13. 13 What is a knowledge graph? A knowledge graph can describe ● a collection of nodes (entities) representing business and fashion entities has_term ● and with labeled relationships between the nodes Product D&G tote bag Attribute Leopard Print Attribute Leopard Spots Attribute Animal Print Properties: Language = “EN” ● each containing information (properties) Properties: ProductID = 123
  • 15. 15 AI: A Multi-Modal Multi-Task Approach Images Text Computer Vision NLP Deep Classifier Example output Product Type: Dress Colour: White Occasion: Wedding Theme: Classic
  • 16. 16 Building a fashion knowledge graph Search Recommendations ... Fashion Knowledge Graph Associates fashion entities with business entities AI Knowledge cleaning Entity resolution Schema mapping Applications Taxonomy & Graph Construction Knowledge Collection Expert Knowledge Data-Driven Insights
  • 17. 17 What did we learn? ✅ Collaborate early with SMEs ✅ Incorporate data-driven insights ✅ Invest in defining a unified taxonomy/graph and share it ✅ Consider perspective: seller vs consumer facing metadata ✅ Use and promote standards where possible
  • 19. Use of Taxonomies in Search ● They help us to understand the domain vocabulary and its structure 19
  • 20. How modern architectures of Search work 20 ● Autocomplete ● Query Understanding ● Query Expansion ● Relevance Re-scorer ● Ranking QueryAutocomplete Query Understanding Query Expansion Relevance Re-scorer Ranking Products Taxonomy
  • 22. How to mix different set of products? 22 Products Query A Query B Query C Query D Products Products Products Products Relevance Re-scorer
  • 23. How to score different expansions? 23 By = λ1 + λ2 C+ λ3 D+ λ4A ● Each expansion gives a score for each product ● Combined score is a is a parameterized model ● Parameters may be trained using click logs and machine learning ● The threshold cut could also be learned
  • 24. 24
  • 25. Challenges ahead ● Internationalization of the taxonomy for other languages ○ Can a knowledge graph help with that? 25