Joe Depeau, Neo4j

1. How to Design Recommendation
Engines with Neo4j
Joe Depeau
Sr. Presales Consultant, UK
6th June, 2018
@joedepeau
http://linkedin.com/in/joedepeau

2. • Recommendations overview
• Why Neo4j?
• What is ‘Recon’?
• Recon Demo
• Q & A
2
Agenda

3. Recommendations
Overview
3

4. Recommendations apply to many industries
• Retail & eCommerce
• Products
• Services
• Media & Content
• Events & Activities
• Social & Dating
• Education
• Travel
• Real Estate

6. 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

7. PRODUCTS
CUSTOMER
EXPERIENCE
THE ONLINE
RETAIL
VALUE
CHAIN

8. PAYMENTS
SALES-
CHANNELS
SUPPLY
CHAIN
PRODUCTS MARKETING
CRM
CUSTOMER
EXPERIENCE
THE ONLINE
RETAIL
VALUE
CHAIN

9. 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

10. Returns
Home
delivery
Inventory
Express
goods
Complaints
reviews
Tweets
Emails
Location/
Adress
KITCHEN
AID
SERIES
Promotions
Bundling

11. Bad Recommendations

12. Why Neo4j?
12

13. 13

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
Good for Analytics, BI, Map
Reduce
Non-Operational, Slow
Queries

15. 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

16. Product CRM Payment Marketing Logistics

17. Blank Slide
17

18. What is ‘Recon’?
18

19. Recon
Tweak &
Monitor
Context
Ordered list of Items
(aka Recommendations)
Recon : A Neo4j Recommendations Framework

20. 20
Recon
Agility &
Flexibility
Accuracy
& Control
Performance
& Scalability

21. Accuracy & Control
• Content filtering
• Collaborative filtering
• Expert/Supervised Intelligence
• Cognitive/Machine Intelligence Easily Plugged In
• Feedback from consumers
• Model transparency

22. 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

23. Performance & Scalability
• Interactive response time
• Massive scale
• Relatively small hardware footprint

24. 24
Let’s have a
look!

25. Q & A
25

26. Thanks for your time!
(the end)
26

27. • Neo4j Recommendation Engine and Product Recommendation
System use cases : https://neo4j.com/use-cases/real-time-
recommendation-engine/
27
Links