Farfetch is the leading global technology platform for the luxury fashion industry. The talk will show Farfetch’s experience in adopting a taxonomy in the fashion domain and how it was applied to improve semantic search methods in our e-commerce search. We’ll show the challenges we’ve had and how we’ve used the fashion taxonomy in our query expansion module to improve search results. In addition, analyzing search usage logs, we will show how we were able to suggest additional examples to the taxonomy development.

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

4. 4
Outfit available from
https://www.farfetch.com

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

6. Categorisation
6

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

9. 9
Perception

10. 10
Subjectivity

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

14. 14
Dots and Lines

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

18. 18
Application to Search

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

21. Query Expansion
21
Products
Products
Products
Products
Products

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

26. 26
Questions
@GeorgeCushen
@PedroBalage
#Farfetch
We’re hiring!