How to Build a Recommendation Engine with Neo4j

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Neo4j

Joe Depeau, Neo4j

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

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

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

PRODUCTS
CUSTOMER
EXPERIENCE
THE ONLINE
RETAIL
VALUE
CHAIN

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

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

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

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

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

20
Recon
Agility &
Flexibility
Accuracy
& Control
Performance
& Scalability

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

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

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

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

Recommendations are at the core of digital transformation in retail today. Whether you’re building features such as product recommendations, promotion recommendations, personalized customer experience, or re-imagining your supply chain to meet customer demands for same day delivery — you’re facing challenges that require the ability to leverage connections from many different data sources, in real-time. There’s no better technology to meet these challenges than a native graphDB technology such as Neo4j.

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