Graphs for Recommendation Engines: Looking beyond Social, Retail, and Media
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We’re all familiar with recommendations in a number of different areas of our lives. Recommendations for social media connections, e-commerce products, or streaming media content are ubiquitous. Perhaps less well known are applications for recommendations in different contexts, like education, HR, fraud, business process management, or offender rehabilitation. In this webinar, we will discuss some of these recommendations use cases in more detail, and look at how graph data can be used to model each domain and power a recommendations engine. We’ll also see an example use case demonstrated using Neo4j.
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Graphs for Recommendation Engines: Looking beyond Social, Retail, and Media
- 1. Graphs for recommendation engines: Looking beyond social, retail, and media Joe Depeau Senior Pre-sales Consultant @joedepeau http://linkedin.com/in/joedepeau
- 2. • Recommendations Overview • Why Neo4j ? • Neo4j Recommendation Framework • The Property Graph model and the Cypher query language • Recommendations use case examples • Demo • Q & A 2 Agenda
- 4. 4 Recommendations Are Everywhere Job Search and Recruiting Financial Services Retail Government Services Healthcare Travel Media and Entertainment Social
- 5. 5 Recommendations Are Everywhere in the Enterprise Human Resources Supplier Analytics Product and BOM Analytics Personalization Customer Journey
- 6. 6 “You May Also Like…” It might sound easy, but there’s a lot going on behind the scenes I hadn’t thought of that! Promotions Blazing Speed Machine Learning Lots of Data Match People and Products Personalization
- 7. 7 Real-Time Recommendations Consider user needs and your business strategies in your recommendations User Perspective Recommend similar and even surprising things by considering: • Past behavior • Similar users • Related things Business Perspective Make recommendations that promote your business strategies: • Diversity • Cost savings • Revenue • Regulatory compliance
- 8. 8 Hybrid Scoring-Based Approach is More Contextual Graph technology enables you to make recommendations that weight multiple methods Collaborative Filtering Based on similar users or things Content Filtering Based on user history and profile Rules-Based Filtering Based on predefined rules and criteria Business Strategy Based on business goals, KPIs, and regulations
- 9. Why Neo4j ?
- 10. • Traverse highly connected data from disparate data sources • Combine with Graph Algorithms • Flexible model • Explainability Why Use Graph Database for Recommendations?
- 11. 11 Neo4j Gives You Control of Your Online Business Self-learning framework improves recommendations over time Customer Context Recommendations Monitor and Adjust Machine Learning Feedback
- 12. 12
- 13. 13 Results with the Neo4j Platform Blazing Speed Thousands of times faster than MySQL Moved from batch to real-time 4ms response time (target was 20ms) Major US retailer Demand-Based Pricing Refresh prices 18 times faster and adjust pricing based on local demand Fortune 200 hotelier Effortless Scalability 90% of $35M+ daily Neo4j transactions Major US retailer Higher Sales In one quarter, digital sales rose 34% to record high Major US retailer Agility Faster, easier development with 90% less code
- 15. 15 Neo4j Intelligent Recommendation Framework Graph-Based Recommendation System • Bases recommendations on flexible scoring- based algorithms • Traverses complex set of relationships and large data sets at blazing speed • Considers both user and business perspectives • Incorporates ML for dynamic segmentation of users and similarity computation • Produces explainable recommendations
- 16. Recommendation Framework Technology Engines Are Processing Pipelines • Pipelines mimic process of building recommendations to reduce query complexity • Easier to develop and maintain • Can enable and disable different parts of the pipeline based on business rules Promotions, inventory, etc. • Ability to score and weight phases differently • Supports traceability and explainability Recommendation Framework Advantages • Faster time to realization • Development and implementation flexibility • Code-free development Highly Configurable Engines • Works with any data model • Highly contextual recommendations for what the user is doing now • Different engines for different uses Discovery Exclude Boost Diversity
- 17. Graph Algorithms Pathfinding and Search Parallel BFS and DFS Shortest Path Single-Source Shortest Path All Pairs Shortest Path Minimum Spanning Tree A* Shortest Path Yen’s K Shortest Path K-Spanning Tree (MST) Random Walk Centrality and Importance Degree Centrality Closeness Centrality Betweenness Centrality PageRank Harmonic Closeness Centrality Dangalchev Closeness Centrality Wasserman and Faust Closeness Centrality Approximate Betweenness Centrality Page Rank, Personalized PageRank, and Article Rank Eigenvector Centrality Community Detection Triangle Count Clustering Coefficients Connected Components (Union Find) Strongly Connected Components Label Propagation Louvain Modularity (1 Step & Multi-Step) Balanced Triad (Identification) Similarity Euclidean Distance Cosine Similarity Jaccard Similarity Overlap Similarity Pearson Similarity Link Prediction Adamic Adar Common Neighbours Preferential Attachment Resource Allocations Same Community Total Neighbours neo4j.com/docs/graph-algorithms/current/ Updated April 2019
- 18. The Property Graph model and the Cypher query language 18
- 19. 19 Car DRIVES name: “Dan” born: May 29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” Anatomy of a Property Graph Database Nodes • Represent the objects in the graph • Can be labeled Relationships • Relate nodes by type and direction Properties • Name-value pairs that can go on nodes and relationships. LOVES LOVES LIVES WITH OW NS Person Person
- 20. 20 MATCH (:Person { name:“Dan”} ) -[:LOVES]-> (beloved) LOVES Dan Ann NODE RELATIONSHIP TYPE LABEL PROPERTY VARIABLE Introducing Cypher
- 22. Retail Recommendations Filter Criteria Price, brand, color… Similar Products Automated bundling and pricing Related Products Logistics
- 23. 23 HR Use Cases Ratings Normalization Succession Planning Building Cross- Functional TeamsFlight Risk Lifetime Employee Value Promotion and Compensation Recommendations
- 24. 24 HR Example: Spotting Future Leaders
- 25. 25 HR Example: Spotting Potential Leavers
- 27. Customer 360 / Customer Journey Recommendations Profitability and Margin Analysis Dynamic CSAT Score Customer Acquisition and Retention Cost Lifetime Customer Value Engagement Analysis and Alerts Churn Score and Analysis
- 28. Personal 360 Recommendations Building Communities / BOFs Engagement Score People Connections CCPA/GDPR Content Recommendations Discover cohorts
- 29. Customers Product and BOM Recommendations Bottleneck analysis Optimized plant assignment MBOM analysis Part/material similarity Inventory analysis Alternate vendors and suppliers Influential part / material discovery
- 30. Customers Government Recommendations Services Offender Rehabilitation Traffic Planning Infrastructure Investment Persons of Interest Documentation and Resources Epidemic Response
- 31. 31 Government Example: Animal Disease Outbreak Response GeoFence
- 32. 32 Government Example: Offender RehabilitationGeoFence Geo Fence
- 33. Q & A
- 34. Thank You