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Improving search experience with a taxonomy in the fashion domain

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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.

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Improving search experience with a taxonomy in the fashion domain

  1. 1. Improving search experience with a taxonomy in the fashion domain George Cushen & Pedro Balage Taxonomy Boot Camp 2019
  2. 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. 3 Building a Taxonomy for Fashion
  4. 4. 4 Outfit available from https://www.farfetch.com
  5. 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. 6. Categorisation 6
  7. 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. 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. 9 Perception
  10. 10. 10 Subjectivity
  11. 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. 12 Associating entities in a Knowledge Graph
  13. 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. 14 Dots and Lines
  15. 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. 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. 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. 18 Application to Search
  19. 19. Use of Taxonomies in Search ● They help us to understand the domain vocabulary and its structure 19
  20. 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. 21. Query Expansion 21 Products Products Products Products Products
  22. 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. 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. 24
  25. 25. Challenges ahead ● Internationalization of the taxonomy for other languages ○ Can a knowledge graph help with that? 25
  26. 26. 26 Questions @GeorgeCushen @PedroBalage #Farfetch We’re hiring!

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