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Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at Farfetch (CDL Meetup)


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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.

Presented at Connected Data London Meetup (October 2019).

Published in: Data & Analytics
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Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at Farfetch (CDL Meetup)

  1. 1. Knowledge Graphs and AI to Hyper- Personalise the Fashion Retail Experience at Farfetch @GeorgeCushen Connected Data London 2019
  2. 2. 2 🎧 DJing = 🔗 Connecting! How can we become superstar data DJs?
  3. 3. 3 Connecting Entities Image: Kelly Sikkema
  4. 4. 4 Outfit available on #Inspire
  5. 5. Image: Paramount Pictures
  6. 6. Farfetch at a glance 6 > 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
  7. 7. 7 Background
  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. 📖 Constructing a unified semantic fashion vocabulary 🏷 Connecting these fashion entities with business entities in a KG via AI 🧬 Inferring 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 Powerful fashion DNA, new knowledge, and insights Data Science Data Science Taxonomy Knowledge Graph Recs Search
  15. 15. 16 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💫?!”
  16. 16. 17 Building a fashion knowledge graph
  17. 17. 18 Perception
  18. 18. 19 Subjectivity
  19. 19. 20 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
  20. 20. 21 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
  21. 21. Skinny 22 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
  22. 22. 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
  23. 23. 24 Deriving new knowledge and insights
  24. 24. 26 Discovering the pearl DELFINA DELETTREZ 'Trillion' earring
  25. 25. 27 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
  26. 26. 28 360o Customer View 360o Customer View Social Email Call CentreClick- stream PoS and ClientelingPurchase History Style Preferences
  27. 27. 29 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
  28. 28. 30 How to perform Deep Walk Image: Jazeen Hollings
  29. 29. 31 How to perform Deep Walk Image: Perozzi et al.
  30. 30. 32 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
  31. 31. Image: Kim Albrecht The Future
  32. 32. 34 Summary
  33. 33. 35 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
  34. 34. 36 Questions @GeorgeCushen @Farfetch We’re hiring!