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.
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Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at Farfetch
- 1. Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at Farfetch @GeorgeCushen Connected Data London 2019
- 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 Background
- 8. 8 Image: Walt Disney Television (Flickr)
- 9. 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?
- 10. 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
- 12. 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
- 13. 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 ...
- 14. 📖 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
- 15. We’re mapping fashion DNA to decode personal style Loosely Structured Data Data Science Data Science Powerful fashion DNA, new knowledge, and insights
- 16. 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
- 17. 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💫?!”
- 18. 18 Building a fashion knowledge graph
- 19. 19 Perception
- 20. 20 Subjectivity
- 21. 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
- 22. Techniques 📷 Computer Vision + 📖 NLP + ✔ Conflation + 👙 Inference + 👥 Crowdsourcing 22
- 23. 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?
- 24. 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
- 26. 26 Richer Product Data Existing catalog External Enrichment Internal Enrichment
- 27. 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
- 28. 28 Deriving new knowledge and insights
- 30. 30 Discovering the pearl DELFINA DELETTREZ 'Trillion' earring
- 31. 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
- 33. Identity Resolution with Graph Analytics 33 Person A Person BPerson A Account 1 Account 2 Account 3 Call Centre Web/App Family A ... ...
- 34. 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
- 35. 35 How to perform Deep Walk Image: Jazeen Hollings
- 36. 36 How to perform Deep Walk Image: Perozzi et al.
- 37. 37 Node2Vec Images: Semantic Scholar, SNAP Stanford
- 38. 38 Graph2Vec Image: Lego Word (wj) Document (d) Document embedding matrix (d-->) Word embedding matrix (wj ) Vocab list of words (V)
- 39. 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
- 41. 41 Summary
- 42. 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