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Similaire à Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at Farfetch (CDL Meetup)
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Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at Farfetch (CDL Meetup)
- 1. Knowledge Graphs and AI to Hyper- Personalise the Fashion Retail Experience at Farfetch @GeorgeCushen Connected Data London 2019
- 2. 2 🎧 DJing = 🔗 Connecting! How can we become superstar data DJs?
- 3. 3 Connecting Entities Image: Kelly Sikkema
- 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 Background
- 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. 📖 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
- 15. 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
- 16. 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💫?!”
- 17. 17 Building a fashion knowledge graph
- 18. 18 Perception
- 19. 19 Subjectivity
- 20. 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
- 21. 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
- 22. 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
- 23. 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
- 24. 24 Deriving new knowledge and insights
- 26. 26 Discovering the pearl DELFINA DELETTREZ 'Trillion' earring
- 27. 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
- 29. 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
- 30. 30 How to perform Deep Walk Image: Jazeen Hollings
- 31. 31 How to perform Deep Walk Image: Perozzi et al.
- 32. 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
- 33. Image: Kim Albrecht The Future
- 34. 34 Summary
- 35. 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