Recommendations are powerful for lead conversion, up-sell and cross-sell. Yet, doing recommendations with traditional databases can be challenging. Multiple join operations will slow down queries so they cannot be used in real-time. Graph data platforms like FlockData can help solve this problem through simple relationship queries.

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TURNING DATA INTO INFORMATION
BUILDING GRAPH-BASED RECOMMENDATION ENGINES

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FlockData is a unified multi-model
data integration and information
management platform for
information storage and retrieval

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Connections Analytics Timeline

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Graph Search Document

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Graph Search Document
Any data source(s)
HTTP RESTful API integration

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Graph Search Document
Any data source(s)
HTTP RESTful API integration

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Graph Search Document
Any data source(s)
HTTP RESTful API integration

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Graph Search Document
Reports Apps Structures
Any data source(s)
HTTP RESTful API integration

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Recommendation Engine
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What is a recommendation engine?
An algorithm that:

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What is a recommendation engine?
Incorporates any number of factors about users
Notably including products or services consumed
Leverages multiple related factors: similar products,
products bought by similar users, etc
Combines these factors as connections
Returns the most relevant (connected) nodes as
recommended products or services
An algorithm that:

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Why build a recommendation engine?

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Why build a recommendation engine?
Original search
Bought together = up-sell
Also bought = up-sell
Targeted ads = cross-sell
Also viewed = conversion

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Why build on graph data?
Only need to specify which
type of relationships to use
As little as 2-lines of query code
Performance:
1M rows —> ~20ms
But very little scale effect
10x-1000x faster than relational
Fast-enough for real-time performance
Efficient and flexible for expanded use

15. ©2013-2015 FlockData LLC - All rights reserved
Why build on graph data?
Only need to specify which
type of relationships to use
As little as 2-lines of query code
Performance:
1M rows —> ~20ms
But very little scale effect
10x-1000x faster than relational
Fast-enough for real-time performance
Efficient and flexible for expanded use
Lookalikes

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Graph as the basis for recommendations

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Graph: connect customers + products

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Step by step
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Start with categories
Product
Features
Scandi-
navian
Family
Living
Room
Used
in:
Style:
Useful
for:

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Map in products
Catalogue
Sofa 2
Product:
Product:
Product
Features
Living
Room
Used
in:
Style:
Useful
for:
Style:
Studio
Used
in:
20-some-
thing
Useful
for: Scandi-
navian
Family
Modern
Sofa 1

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Map in purchases
Customer
A
Catalogue
Sofa 2
Product:
Product:
Product
Features
Living
Room
Used
in:
Style:
Useful
for:
Style:
Studio
Used
in:
20-some-
thing
Useful
for:
BOUGHT
Scandi-
navian
Family
Modern
Sofa 1

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Learn characteristics
Customer
A
Catalogue
Sofa 2
Product:
Product:
Product
Features
Family
Living
Room
Used
in:
Style:
Useful
for:
Style:
Studio
Used
in:
Useful
for:
BOUGHT
DEMOGRAPHIC
LIKES
Scandi-
navian
Modern
Sofa 1
20-some-
thing

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Recognize new customer features
Customer
A
Catalogue
Sofa 2
Product:
Product:
Product
Features
Living
Room
Used
in:
Style:
Useful
for:
Style:
Studio
Used
in:
Useful
for:
BOUGHT
DEMOGRAPHIC
LIKES
Scandi-
navian
Sofa 1
20-some-
thing
Customer
A
LIKES
DEMOGRAPHIC
Family
Modern

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Make recommendations
Customer
A
Catalogue
Sofa 2
Product:
Product:
Product
Features
Living
Room
Used
in:
Style:
Useful
for:
Style:
Studio
Used
in:
Useful
for:
BOUGHT
DEMOGRAPHIC
LIKES
Scandi-
navian
Sofa 1
20-some-
thing
Customer
A
LIKES
DEMOGRAPHIC
RECOMMEND
Family
Modern