How to Design Retail Recommendation Engines with Neo4j

Recommendation EnginesBuilding Powerful Recommendation Engines for Retail With Neo4j

Alessandro SvenssonSolutions @ Neo4j

William LyonDeveloper Relations @ Neo4j

First of all…

Relational Database

This is data modelled as graph!

Graph Database

Powerful, real-time, recommendations and personalization engines have become

fundamental for creating superior user experience and commercial success in retail

Recommendation Engines

Creating Relevance in an Ocean of Possibilities

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

Product Recommendations

Effective product recommendation algorithms has become the new standard in online retail — directly affecting revenue streams and the shopping experience.

Logistics/DeliveryRouting 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

Powerful recommendation engines rely on the connections between

multiple sources of data

How To Build Recommendation Engines For Retail with Neo4j

Neo4j in Action

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

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The Store

Search

Hi, loginMy Account

People who bought Side Table also bought:

Coffee Table

$235Low Book Shelf

$150Bed 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, loginMy AccountSearch

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All departments Living room | Kitchen | Hallway | Lightning | Bedroom | Garden | Home Office Space

Tr a c k O r d e r s | G i f t C a r d s | S t o r e fin d e r | C r e d i t C a r d | G r o c e r y P i c k u p | H e l p

Wood Side Table

$110Green Side Table

$135Walnut Side Table

$120Coffee Table

$235Low Book Shelf

$150Bed Side Table

$90




The Store

Search

Hi, loginMy Account


Coffee Table

$235Low Book Shelf

$150Bed Side Table

$90

Mobile Brick & MortarWeb

The Store






Wood Side Table



$120Coffee Table

$235Low Book Shelf

$150Bed Side Table

$90



The Store

Search

Hi, loginMy Account


Coffee Table

$235Low Book Shelf

$150Bed Side Table

$90


The Store






Wood Side Table



$120Coffee Table

$235Low Book Shelf

$150Bed Side Table

$90



The Store

Search

Hi, loginMy Account


Coffee Table

$235Low Book Shelf

$150Bed Side Table

$90


The Store






Wood Side Table



$120Coffee Table

$235Low Book Shelf

$150Bed Side Table

$90

The Store






Wood Side Table



$120Coffee Table

$235Low Book Shelf

$150Bed Side Table

$90

Personalized Promotions Personalized Real-Time Recommendations

Personalized Real-Time Recommendations

The Store






Wood Side Table



$120Coffee Table

$235Low Book Shelf

$150Bed Side Table

$90

Data-Model (Expressed as

a graph)

Category

CategoryProduct

Product

Product

Collaborative FilteringAn algorithm that considers users interactions with products, with the

assumption that other users will behave in similar ways.

Algorithm Types

Content BasedAn algorithm that considers

similarities between products and categories of products.

Customer

Customer

Product

Product

Product

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

Purchases


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


Location

Purchases


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


Location

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 ReduceNon-Operational, Slow Queries

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

William LyonDeveloper Relations @ Neo Technology

Neo4j DEMOHow 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.

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

Routing Recommendations

Don’t Take Our Word For ItExamples 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


PersonalizationEngines

Adidas

Case Studies

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

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

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

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

Towards Graph Inevitability

“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.”


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


Valuable Resources!

neo4jsandbox.com neo4j.com/industries/retail/ neo4j.com/product

Sandbox Retail Solutions Product

http://neo4j.com/sandbox-v2

http://neo4j.com/industries/retail

http://neo4j.com/product

Thank you!