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.

1. Recommendation EnginesBuilding Powerful Recommendation Engines for Retail With Neo4j

2. Alessandro Svensson
Solutions @ Neo4j
William Lyon
Developer Relations @ Neo4j

3. First of all…

4. Relational
Database

5. This is data modelled as graph!
Graph
Database

6. Powerful, real-time, recommendations and
personalization engines have become
fundamental for creating superior user experience
and commercial success in retail
Recommendation Engines

7. Creating Relevance in an
Ocean of Possibilities

8. How Graph Based Recommendations
Transformed the Consumer Web
People Graph
“People you may know”
Disruptor: Facebook
Industry: Media Ad-business
Disruptor: Amazon
Industry: Retail
People & Products
“Other people also bought”
People & Content
“You might also like”
Disruptor: Netflix
Industry: Broadcasting Media

9. Product
Recommendations
Effective product recommendation
algorithms has become the new
standard in online retail — directly
affecting revenue streams and the
shopping experience.
Logistics/Delivery
Routing recommendations allows
companies to save money on routing
and delivery, and provide better and
faster service.
Promotion
recommendations
Building powerful personalized
promotion engines is another area
within retail that requires input from
multiple data sources, and real-time,
session based queries, which is an
ideal task to solve with Neo4j.
Today Recommendation Engines are At the
Core of Digitization in Retail

10. Powerful recommendation engines
rely on the connections between
multiple sources of data

11. How To Build Recommendation
Engines For Retail with Neo4j
Neo4j in Action

12. What are the Challenges from a
Data Point of View in Retail Today?

13. Dreamhouse
Series 15% off
The Store
Search
Hi, login
My Account
People who bought Side Table also bought:
Coffee Table
$235
Low Book Shelf
$150
Bed Side Table
$90
Mobile Brick & Mortar
Multi-Channel
Web
The
Store
People who bought Side Table also bought: Similar product in from Home Office Series:
Hi, login
My AccountSearch
Dreamhouse Series 15% off
All departments Living room | Kitchen | Hallway | Lightning | Bedroom | Garden | Home Office Space
Tra c k O rd e r s | G i f t C a rd s | S t o re fi n d e r | C re d i t C a rd | G ro c e r y P i c k u p | H e lp
Wood Side Table
$110
Green Side Table
$135
Walnut Side Table
$120
Coffee Table
$235
Low Book Shelf
$150
Bed Side Table
$90
Product Recommendations

14. Dreamhouse
Series 15% off
The Store
Search
Hi, login
My Account
People who bought Side Table also bought:
Coffee Table
$235
Low Book Shelf
$150
Bed Side Table
$90
Mobile Brick & Mortar
Web
The
Store
People who bought Side Table also bought: Similar product in from Home Office Series:
Hi, login
My AccountSearch
Dreamhouse Series 15% off
All departments Living room | Kitchen | Hallway | Lightning | Bedroom | Garden | Home Office Space
Tra c k O rd e r s | G i f t C a rd s | S t o re fi n d e r | C re d i t C a rd | G ro c e r y P i c k u p | H e lp
Wood Side Table
$110
Green Side Table
$135
Walnut Side Table
$120
Coffee Table
$235
Low Book Shelf
$150
Bed Side Table
$90

15. Dreamhouse
Series 15% off
The Store
Search
Hi, login
My Account
People who bought Side Table also bought:
Coffee Table
$235
Low Book Shelf
$150
Bed Side Table
$90
Mobile Brick & Mortar
Web
The
Store
People who bought Side Table also bought: Similar product in from Home Office Series:
Hi, login
My AccountSearch
Dreamhouse Series 15% off
All departments Living room | Kitchen | Hallway | Lightning | Bedroom | Garden | Home Office Space
Tra c k O rd e r s | G i f t C a rd s | S t o re fi n d e r | C re d i t C a rd | G ro c e r y P i c k u p | H e lp
Wood Side Table
$110
Green Side Table
$135
Walnut Side Table
$120
Coffee Table
$235
Low Book Shelf
$150
Bed Side Table
$90

16. Dreamhouse
Series 15% off
The Store
Search
Hi, login
My Account
People who bought Side Table also bought:
Coffee Table
$235
Low Book Shelf
$150
Bed Side Table
$90
Mobile Brick & Mortar
Web
The
Store
People who bought Side Table also bought: Similar product in from Home Office Series:
Hi, login
My AccountSearch
Dreamhouse Series 15% off
All departments Living room | Kitchen | Hallway | Lightning | Bedroom | Garden | Home Office Space
Tra c k O rd e r s | G i f t C a rd s | S t o re fi n d e r | C re d i t C a rd | G ro c e r y P i c k u p | H e lp
Wood Side Table
$110
Green Side Table
$135
Walnut Side Table
$120
Coffee Table
$235
Low Book Shelf
$150
Bed Side Table
$90

17. The
Store
People who bought Side Table also bought: Similar product in from Home Office Series:
Hi, login
My AccountSearch
Dreamhouse Series 15% off
All departments Living room | Kitchen | Hallway | Lightning | Bedroom | Garden | Home Office Space
Tra c k O rd e r s | G i f t C a rd s | S t o re fi n d e r | C re d i t C a rd | G ro c e r y P i c k u p | H e lp
Wood Side Table
$110
Green Side Table
$135
Walnut Side Table
$120
Coffee Table
$235
Low Book Shelf
$150
Bed Side Table
$90
Personalized Promotions Personalized Real-Time
Recommendations
Personalized Real-Time
Recommendations

18. People who bought Side Table also bought: Similar product in from Home Office Series:
Wood Side Table
$110
Green Side Table
$135
Walnut Side Table
$120
Coffee Table
$235
Low Book Shelf
$150
Bed Side Table
$90
Data-Model
(Expressed as
a graph)
Category
Category
Product
Product
Product
Collaborative Filtering
An algorithm that considers users
interactions with products, with the
assumption that other users will
behave in similar ways.
Algorithm Types
Content Based
An algorithm that considers
similarities between products and
categories of products.
Customer
Customer
Product
Product
Product

19. Category Price ConfigurationsLocation
Silos & Polyglot Persistence
Purchase ViewReviewReturn In-store PurchasesInventory
Products Customers / Users
Location
Purchases
RELATIONAL DB WIDE COLUMN STORE
Views
DOCUMENT STORE
User Review
RELATIONAL DB
In-Store
Purchase
Shopping
Cart
KEY VALUE STORE
Product
Catalogue
DOCUMENT STORE

20. Purchases
RELATIONAL DB WIDE COLUMN STORE
Views
DOCUMENT STORE
User Review
RELATIONAL DB
In-Store
Purchase
Shopping
Cart
KEY VALUE STORE
Product
Catalogue
DOCUMENT STORE
Silos & Polyglot Persistence
Category Price ConfigurationsLocation Purchase ViewReviewReturn In-store PurchasesInventory
Products Customers / Users
Location

21. Purchases
RELATIONAL DB WIDE COLUMN STORE
Views
DOCUMENT STORE
User Review
RELATIONAL DB
In-Store
Purchase
Shopping
Cart
KEY VALUE STORE
Product
Catalogue
DOCUMENT STORE
Category Price ConfigurationsLocation
Polyglot Persistence
Purchase ViewReviewReturn In-store PurchasesInventory LocationCategory Price ConfigurationsLocation Purchase ViewReviewReturn In-store PurchasesInventory
Products Customers / Users
Location

22. Data Lake
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
Recommendations require an operational
workload — it’s in the moment, real-time!
Good for Analytics, BI, Map Reduce
Non-Operational, Slow Queries

23. 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
Drivers: Java | JavaScript | Python | .Net | PHP | Go | Ruby
Apps and Systems
Real-Time
Queries

24. William Lyon
Developer Relations @ Neo Technology
Neo4j DEMO
How can import data from different data
sources, using Cypher — the query
language for Neo4j — and demonstrate
both content-based and collaborative
filtering recommendations using this data.

25. Why Graph Based
Recommendation Engines?
• Increase revenue
• Create Higher Engagement
• Mitigate RiskValue
• Real-Time capabilities
• Ability to use the most recent transaction data
• Flexibility to incorporate new data sources
Performance

26. Routing
Recommendations
Don’t Take Our Word For It
Examples of companies that use Neo4j, the world’s leading graph
database, for recommendation and personalization engines.
Adidas uses Neo4j to combine content and
product data into a single, searchable graph
database which is used to create a
personalized customer experience
“We have many different silos, many different
data domains, and in order to make sense
out of our data, we needed to bring those
together and make them useful for us,”
– Sokratis Kartelias, Adidas
eBay Now Tackles eCommerce Delivery
Service Routing with Neo4j
“We needed to rebuild when growth and new
features made our slowest query longer than
our fastest delivery - 15 minutes! Neo4j gave
us best solution”
– Volker Pacher, eBay
Walmart uses Neo4j to give customer best
web experience through relevant and
personal recommendations
“As the current market leader in graph
databases, and with enterprise features for
scalability and availability, Neo4j is the right
choice to meet our demands”.
- Marcos Vada, Walmart
Product
Recommendations
Personalization
Engines
Adidas

27. Case Studies

28. Case studySolving real-time recommendations for the
World’s largest retailer.
Challenge
• In its drive to provide the best web experience for its
customers, Walmart wanted to optimize its online
recommendations.
• Walmart recognized the challenge it faced in
delivering recommendations with traditional relational
database technology.
• Walmart uses Neo4j to quickly query customers’ past
purchases, as well as instantly capture any new
interests shown in the customers’ current online visit –
essential for making real-time recommendations.
Use of Neo4j
“As the current market leader in
graph databases, and with
enterprise features for scalability
and availability, Neo4j is the right
choice to meet our demands”.
- Marcos Vada, Walmart
• With Neo4j, Walmart could substitute a heavy batch
process with a simple and real-time graph database.
Result/Outcome

29. adidas Case studyCombining content and product data into Neo4j to create
personalized customer experience
Challenge
• Data was stored and managed in disparate silos,
preventing Adidas from getting a holistic view of
costumers
• On the technical level, data models didn’t align
between the information silos, and there wasn’t a
standard, consistent way to communicate between
the different data domains.
• Adidas uses Neo4j to combine content and product
data into a single, searchable graph database which
is used to create a personalized customer experience
• They created a meta-data repository that stored and
queried data-relationships in Neo4j, without having to
replace existing data-sources.
Use of Neo4j
• With a vast global audience, the adidas Group
significantly improved their ability to provide a more
personalized experience to its online shoppers.
• The Neo4j graph database proved to the be the
ideal technology for creating the Service, offering
access and searchability to all data, along with
support for new emerging services.
“We have many different silos, many
different data domains, and in order
to make sense out of our data, we
needed to bring those together and
make them useful for us,”
Result/Outcome
– Sokratis Kartelias

30. Case studyeBay Now Tackles eCommerce Delivery Service Routing
with Neo4j
Challenge
• The queries used to select the best courier for eBays
routing system were simply taking too long and they
needed a solution to maintain a competitive service.
• The MySQL joins being used created a code base too
slow and complex to maintain.
• eBay is now using Neo4j’s graph database platform
to redefine e-commerce, by making delivery of online
and mobile orders quick and convenient.
Use of Neo4j
• With Neo4j eBay managed to eliminate the biggest
roadblock between retailers and online shoppers:
the option to have your item delivered the same day.
• The schema-flexible nature of the database allowed
easy extensibility, speeding up development.
• Neo4j solution was more than 1000x faster than the
prior MySQL Soltution.
Our Neo4j solution is literally
thousands of times faster than the
prior MySQL solution, with queries
that require 10-100 times less code.
Result/Outcome
– Volker Pacher, eBay

31. Top Tier US Retailer
Case studySolving Real-time promotions for a top US
retailer
Challenge
• Suffered significant revenues loss, due to legacy
infrastructure.
• Particularly challenging when handling transaction
volumes on peak shopping occasions such as
Thanksgiving and Cyber Monday.
• Neo4j is used to revolutionize and reinvent its real-
time promotions engine.
• On an average Neo4j processes 90% of this retailer’s
35M+ daily transactions, each 3-22 hops, in 4ms or
less.
Use of Neo4j
• Reached an all time high in online revenues, due to
the Neo4j-based friction free solution.
• Neo4j also enabled the company to be one of the
first retailers to provide the same promotions across
both online and traditional retail channels.
“On an average Neo4j processes
90% of this retailer’s 35M+ daily
transactions, each 3-22 hops, in
4ms or less.”
– Top Tier US Retailer
Result/Outcome

32. Towards Graph Inevitability

33. “Graph analysis is possibly the single most effective
competitive differentiator for organizations pursuing
data-driven operations and decisions after the design
of data capture.
“By the end of 2018, 70% of leading organizations
will have one or more pilot or proof-of-concept
efforts underway utilizing graph databases.”
Towards Graph Inevitability

34. “Forrester estimates that over 25% of enterprises
will be using graph databases by 2017.”
Towards Graph Inevitability

35. Valuable Resources!
neo4jsandbox.com neo4j.com/industries/retail/ neo4j.com/product
Sandbox Retail Solutions Product

36. Thank you!