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How to Build a Recommendation Engine with Neo4j

Joe Depeau, Neo4j

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How to Build a Recommendation Engine with Neo4j

  1. 1. How to Design Recommendation Engines with Neo4j Joe Depeau Sr. Presales Consultant, UK 6th June, 2018 @joedepeau http://linkedin.com/in/joedepeau
  2. 2. • Recommendations overview • Why Neo4j? • What is ‘Recon’? • Recon Demo • Q & A 2 Agenda
  3. 3. Recommendations Overview 3
  4. 4. Recommendations apply to many industries • Retail & eCommerce • Products • Services • Media & Content • Events & Activities • Social & Dating • Education • Travel • Real Estate
  5. 5. Goals of a good Recommender System • Relevance • Users are more likely to consume Items they find interesting • Novelty • Items they didn’t see/buy/view in the past (related) • Serendipity • Unexpected Items, a lucky discovery (unrelated) • Increasing recommendation diversity • Same type of items increases the risk of the User not liking any Item
  6. 6. PRODUCTS CUSTOMER EXPERIENCE THE ONLINE RETAIL VALUE CHAIN
  7. 7. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCE THE ONLINE RETAIL VALUE CHAIN
  8. 8. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCEStore Mobile Shipping Inventory Express goods Home delivery Ratings Price-range Category Content Promotions Online advertising Loyalty Programs Returns Feedback reviews Tweets Emails Customer support Credit Card Cash Mobile Pay Purchase History Augmented Reality Webstore BEYOND MOBILE Smart products Connected homes
  9. 9. Returns Home delivery Inventory Express goods Complaints reviews Tweets Emails Location/ Adress KITCHEN AID SERIES Promotions Bundling
  10. 10. Bad Recommendations
  11. 11. Why Neo4j? 12
  12. 12. 13
  13. 13. Silos & Polyglot Solutions Purchases RELATIONAL DB Product Catalogue DOCUMENT STORE WIDE COLUMN STORE Views DOCUMENT STORE User Review RELATIONAL DB In-Store Purchase Shopping Cart KEY VALUE STORE Good for Analytics, BI, Map Reduce Non-Operational, Slow Queries
  14. 14. Silos & Polyglot Solutions Purchases RELATIONAL DB Product Catalogue DOCUMENT STORE WIDE COLUMN STORE Views DOCUMENT STORE User Review RELATIONAL DB In-Store Purchase Shopping Cart KEY VALUE STORE Connector Real-Time Queries
  15. 15. Product CRM Payment Marketing Logistics
  16. 16. Blank Slide 17
  17. 17. What is ‘Recon’? 18
  18. 18. Recon Tweak & Monitor Context Ordered list of Items (aka Recommendations) Recon : A Neo4j Recommendations Framework
  19. 19. 20 Recon Agility & Flexibility Accuracy & Control Performance & Scalability
  20. 20. Accuracy & Control • Content filtering • Collaborative filtering • Expert/Supervised Intelligence • Cognitive/Machine Intelligence Easily Plugged In • Feedback from consumers • Model transparency
  21. 21. Agile & Flexible • Evolve & tune model and recommendations organically, in response to changing business requirements, with no downtime • Integrate with cognitive/machine learning and NLP environment • Scoring features can be easily extended • Create & tune rules and incorporate feedback "on the fly" • Deploy quickly • Recommendation Engine can be reverse engineered from existing data sources
  22. 22. Performance & Scalability • Interactive response time • Massive scale • Relatively small hardware footprint
  23. 23. 24 Let’s have a look!
  24. 24. Q & A 25
  25. 25. Thanks for your time! (the end) 26
  26. 26. • Neo4j Recommendation Engine and Product Recommendation System use cases : https://neo4j.com/use-cases/real-time- recommendation-engine/ 27 Links

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