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Knowledge Graphs and AI to
Hyper-Personalise the Fashion
Retail Experience at Farfetch
@GeorgeCushen
Connected Data London...
2
Image: Kelly Sikkema
3
Outfit available from
https://www.farfetch.com
Image: Paramount Pictures
Farfetch at a glance
5
> 3,000*
Employees across 13 countries
$1.4 Billion*
Gross Merchandise Value
> 3,000*
Brands availa...
6
Background
8
Image: Walt Disney Television (Flickr)
A New Perspective: Emphasising Relationships
● Businesses and their products/services are all about Entities and Relations...
10
What is a knowledge graph?
A knowledge graph can describe
● a collection of nodes (entities) representing business and ...
11
Dots and Lines
12
Why use a knowledge graph?
● Have naturally highly connected-data
● Derive new insights with Graph Analysis & Graph-bas...
Where Business Meets Fashion
A domain specific knowledge graph for fashion.
Business vs Fashion Entities
Business Fashion
...
📖 Constructs a unified semantic fashion vocabulary
🏷 Connects these fashion entities with business entities in a KG via AI...
We’re mapping fashion DNA to decode personal style
Loosely Structured
Data
Data Science Data Science
Powerful fashion
DNA,...
16
Example Use Cases
Free Text Search
Increase product discovery with
synonyms and rich attributes for
material, occasion ...
17
Communicating a graph
Product Managers
“How can we improve the
customer experience?”
“How can we increase
GMV/revenue?”...
18
Building a fashion knowledge graph
19
Perception
20
Subjectivity
21
Building a fashion knowledge graph
Search Recommendations ...
Fashion Knowledge Graph
Associates fashion entities with ...
Techniques
📷 Computer Vision +
📖 NLP +
✔ Conflation +
👙 Inference +
👥 Crowdsourcing
22
23
AI: A Multi-Modal Multi-Task Approach
Images Text
Computer
Vision NLP
Deep
Classifier
Example output
Product Type: Dres...
Skinny
24
Universal Fashion Taxonomy
Fashion
Taxonomy
Synonyms
Descriptive
attributes
Brand DNA
Materials
ColoursTrends
Ed...
Synonym Enrichment
Padded
coat
Down
coat
Duvet coat
Quilted
coat
Puffer
jacket
Down-filled
jacket
Down
jacket
Quilted
jack...
26
Richer Product Data
Existing
catalog
External Enrichment
Internal Enrichment
27
Richer Product Data
Existing catalog
data
AI predicts richer and
more diverse attributes to
help construct the graph
Gr...
28
Deriving new knowledge and insights
30
Discovering the pearl
DELFINA DELETTREZ 'Trillion' earring
31
Features from Graphs
Extract features from the graph such as:
● nodes
○ degree
● pairs
○ number of common neighbours
● ...
32
360o
Customer View
360o
Customer
View
Social
Email
Call
CentreClick-
stream
PoS
and
ClientelingPurchase
History
Style
P...
Identity Resolution with Graph Analytics
33
Person A Person BPerson A
Account 1 Account 2 Account 3
Call
Centre
Web/App
Fa...
34
What is Deep Walk?
Learn a latent representation of adjacency matrices
using deep learning based language processing.
●...
35
How to perform Deep Walk
Image: Jazeen Hollings
36
How to perform Deep Walk
Image: Perozzi et al.
37
Node2Vec
Images: Semantic Scholar, SNAP Stanford
38
Graph2Vec
Image: Lego
Word (wj)
Document (d)
Document embedding matrix (d-->)
Word embedding matrix (wj
)
Vocab list of...
39
Vertex and Graph Embeddings
Vertex embedding approaches:
DeepWalk, Node2Vec, LLE, Laplacian Eigenmaps, Graph Factorizat...
Image: Kim Albrecht
41
Summary
42
Takeaways
● Graphs can offer a new, democratised
perspective on enterprise data
● When graph based analytics and AI
are...
43
Questions
@GeorgeCushen
#Farfetch
We’re hiring!
Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at Farfetch
Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at Farfetch
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Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at Farfetch

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What is the key to the holistic success of the fastest growing and most successful companies of our time globally? Well, often the key is the rapid increase in collected and analysed data. Graph databases provide a way to organise semantically by classes, not tables, are web-aware, and are superior for handling deep, complex relationships than traditional relational or NoSQL data stores.

It is these deep, complex relationships that can provide the rich context for hyper-personalising your product offering, inspiring consumers to purchase. In this talk, we describe how we are using artificial intelligence at Farfetch to not only help build a knowledge graph but also to evolve our insights with state-of-the-art graph-based AI.

Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at Farfetch

  1. 1. Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at Farfetch @GeorgeCushen Connected Data London 2019
  2. 2. 2 Image: Kelly Sikkema
  3. 3. 3 Outfit available from https://www.farfetch.com
  4. 4. Image: Paramount Pictures
  5. 5. Farfetch at a glance 5 > 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
  6. 6. 6 Background
  7. 7. 8 Image: Walt Disney Television (Flickr)
  8. 8. A New Perspective: Emphasising Relationships ● Businesses and their products/services are all about Entities and Relationships ● Examples of entities and relationships in industry: Farfetch Consumer searches Product with Terms Amazon Seller sells Product to Consumer Uber Driver provides Trip to Rider Facebook Person shares Status with Friend ● How can we represent, analyse, and visualise this kind of data?
  9. 9. 10 What is a knowledge graph? A knowledge graph can describe ● a collection of nodes (entities) representing business and fashion entities has_term has_synonym has_child Properties: Inherit = true ● 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
  10. 10. 11 Dots and Lines
  11. 11. 12 Why use a knowledge graph? ● Have naturally highly connected-data ● Derive new insights with Graph Analysis & Graph-based AI ● Enable stakeholders to easily visualise relationships and make informed decisions ● Flexible schema to facilitate evolution to expand business entities ● Optimized for storing and querying graphs ○ Significantly faster than SQL databases for querying relationships ○ Relationships are a fundamental structure, so following relationships is a single lookup, making this operation blazingly fast
  12. 12. 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 ...
  13. 13. 📖 Constructs a unified semantic fashion vocabulary 🏷 Connects these fashion entities with business entities in a KG via AI 🧬 Infers DNA from the relationships in the Knowledge Graph (KG) We’re mapping fashion DNA to decode personal style
  14. 14. We’re mapping fashion DNA to decode personal style Loosely Structured Data Data Science Data Science Powerful fashion DNA, new knowledge, and insights
  15. 15. 16 Example Use Cases Free Text Search Increase product discovery with synonyms and rich attributes for material, occasion (e.g. skiing), etc. Semantic Search Increase product discovery based by using graph to understand consumer’s intent Ranking Leverage rich product connections to increase relevance on listing pages Recommendations Increase relevance based on richer product attributes and deep graph relationships
  16. 16. 17 Communicating a graph Product Managers “How can we improve the customer experience?” “How can we increase GMV/revenue?” Data Scientists “Wow, looks like a NN, hold my Pandas 🐼🐼🐼, I’m onboard!!” Backend Engineers “Why do we need a graph?” “Which graph database meets the requirements?” Data Engineers “Is your Airflow dizzy🥴😵? It’s traversing through cyclic connections💫?!”
  17. 17. 18 Building a fashion knowledge graph
  18. 18. 19 Perception
  19. 19. 20 Subjectivity
  20. 20. 21 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
  21. 21. Techniques 📷 Computer Vision + 📖 NLP + ✔ Conflation + 👙 Inference + 👥 Crowdsourcing 22
  22. 22. 23 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 Embeddings? NER? Coreference resolution? Relationship extraction?
  23. 23. Skinny 24 Universal Fashion Taxonomy Fashion Taxonomy Synonyms Descriptive attributes Brand DNA Materials ColoursTrends Editorial, emotive, seasonal concepts Textile Cotton Denim Product 2 Swedish Design Acne Connected Data Conferen ce Autumn Product 1 PrintsCircles Blue Light Blue
  24. 24. Synonym Enrichment 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
  25. 25. 26 Richer Product Data Existing catalog External Enrichment Internal Enrichment
  26. 26. 27 Richer Product Data Existing catalog data AI predicts richer and more diverse attributes to help construct the graph Graph based AI and analytics further enrich attributes and infer product DNA Qualityof ProductDNA RichproductDNA
  27. 27. 28 Deriving new knowledge and insights
  28. 28. 30 Discovering the pearl DELFINA DELETTREZ 'Trillion' earring
  29. 29. 31 Features from Graphs Extract features from the graph such as: ● nodes ○ degree ● pairs ○ number of common neighbours ● groups ○ custer assignments ● Infer DNA ● Link Prediction ● Anomaly Prediction ● Clustering ● ... Adjacency Matrix
  30. 30. 32 360o Customer View 360o Customer View Social Email Call CentreClick- stream PoS and ClientelingPurchase History Style Preferences
  31. 31. Identity Resolution with Graph Analytics 33 Person A Person BPerson A Account 1 Account 2 Account 3 Call Centre Web/App Family A ... ...
  32. 32. 34 What is Deep Walk? Learn a latent representation of adjacency matrices using deep learning based language processing. ● Infer DNA ● Link Prediction ● Anomaly Prediction ● Clustering ● ... Adjacency Matrix Latent Representation
  33. 33. 35 How to perform Deep Walk Image: Jazeen Hollings
  34. 34. 36 How to perform Deep Walk Image: Perozzi et al.
  35. 35. 37 Node2Vec Images: Semantic Scholar, SNAP Stanford
  36. 36. 38 Graph2Vec Image: Lego Word (wj) Document (d) Document embedding matrix (d-->) Word embedding matrix (wj ) Vocab list of words (V)
  37. 37. 39 Vertex and Graph Embeddings Vertex embedding approaches: DeepWalk, Node2Vec, LLE, Laplacian Eigenmaps, Graph Factorization, GraRep, HOPE, DNGR, GCN, LINE Graph embedding approaches: Graph2Vec, Patchy-san, sub2vec, WL kernel, Deep WL kernels Image: rocknwool on Unsplash
  38. 38. Image: Kim Albrecht
  39. 39. 41 Summary
  40. 40. 42 Takeaways ● Graphs can offer a new, democratised perspective on enterprise data ● When graph based analytics and AI are performed on connected data, we can derive powerful new knowledge and insights ● Which can drive hyper-personalisation, improving the customer experience
  41. 41. 43 Questions @GeorgeCushen #Farfetch We’re hiring!
  • GeorgeCushen

    Oct. 24, 2019

What is the key to the holistic success of the fastest growing and most successful companies of our time globally? Well, often the key is the rapid increase in collected and analysed data. Graph databases provide a way to organise semantically by classes, not tables, are web-aware, and are superior for handling deep, complex relationships than traditional relational or NoSQL data stores. It is these deep, complex relationships that can provide the rich context for hyper-personalising your product offering, inspiring consumers to purchase. In this talk, we describe how we are using artificial intelligence at Farfetch to not only help build a knowledge graph but also to evolve our insights with state-of-the-art graph-based AI.

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