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Recommendation EnginesBuilding Powerful Recommendation Engines for Retail With Neo4j
Alessandro Svensson
Solutions @ Neo4j
William Lyon
Developer 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 experi...
Creating Relevance in an
Ocean of Possibilities
How Graph Based Recommendations
Transformed the Consumer Web
People Graph
“People you may know”
Disruptor: Facebook
Indust...
Product
Recommendations
Effective product recommendation
algorithms has become the new
standard in online retail — directl...
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?
Dreamhouse
Series 15% off
The Store
Search
Hi, login
My Account
People who bought Side Table also bought:
Coffee Table
$23...
Dreamhouse
Series 15% off
The Store
Search
Hi, login
My Account
People who bought Side Table also bought:
Coffee Table
$23...
Dreamhouse
Series 15% off
The Store
Search
Hi, login
My Account
People who bought Side Table also bought:
Coffee Table
$23...
Dreamhouse
Series 15% off
The Store
Search
Hi, login
My Account
People who bought Side Table also bought:
Coffee Table
$23...
The
Store
People who bought Side Table also bought: Similar product in from Home Office Series:
Hi, login
My AccountSearch
D...
People who bought Side Table also bought: Similar product in from Home Office Series:
Wood Side Table
$110
Green Side Table
...
Category Price ConfigurationsLocation
Silos & Polyglot Persistence
Purchase ViewReviewReturn In-store PurchasesInventory
Pr...
Purchases
RELATIONAL DB WIDE COLUMN STORE
Views
DOCUMENT STORE
User Review
RELATIONAL DB
In-Store
Purchase
Shopping
Cart
K...
Purchases
RELATIONAL DB WIDE COLUMN STORE
Views
DOCUMENT STORE
User Review
RELATIONAL DB
In-Store
Purchase
Shopping
Cart
K...
Data Lake
Purchases
RELATIONAL DB
Product
Catalogue
DOCUMENT STORE WIDE COLUMN STORE
Views
DOCUMENT STORE
User Review
RELA...
Purchases
RELATIONAL DB
Product
Catalogue
DOCUMENT STORE WIDE COLUMN STORE
Views
DOCUMENT STORE
User Review
RELATIONAL DB
...
William Lyon
Developer Relations @ Neo Technology
Neo4j DEMO
How can import data from different data
sources, using Cypher ...
Why Graph Based
Recommendation Engines?
• Increase revenue
• Create Higher Engagement
• Mitigate RiskValue
• Real-Time cap...
Routing
Recommendations
Don’t Take Our Word For It
Examples of companies that use Neo4j, the world’s leading graph
databas...
Case Studies
Case studySolving real-time recommendations for the
World’s largest retailer.
Challenge
• In its drive to provide the best...
adidas Case studyCombining content and product data into Neo4j to create
personalized customer experience
Challenge
• Data...
Case studyeBay Now Tackles eCommerce Delivery Service Routing
with Neo4j
Challenge
• The queries used to select the best c...
Top Tier US Retailer
Case studySolving Real-time promotions for a top US
retailer
Challenge
• Suffered significant revenues...
Towards Graph Inevitability
“Graph analysis is possibly the single most effective
competitive differentiator for organizations pursuing
data-driven op...
“Forrester estimates that over 25% of enterprises
will be using graph databases by 2017.”
Towards Graph Inevitability
Valuable Resources!
neo4jsandbox.com neo4j.com/industries/retail/ neo4j.com/product
Sandbox Retail Solutions Product
Thank you!
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How to Design Retail Recommendation Engines with Neo4j

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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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How to Design Retail Recommendation Engines with Neo4j

  1. 1. Recommendation EnginesBuilding Powerful Recommendation Engines for Retail With Neo4j
  2. 2. Alessandro Svensson Solutions @ Neo4j William Lyon Developer Relations @ Neo4j
  3. 3. First of all…
  4. 4. Relational Database
  5. 5. This is data modelled as graph! Graph Database
  6. 6. Powerful, real-time, recommendations and personalization engines have become fundamental for creating superior user experience and commercial success in retail Recommendation Engines
  7. 7. Creating Relevance in an Ocean of Possibilities
  8. 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. 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. 10. Powerful recommendation engines rely on the connections between multiple sources of data
  11. 11. How To Build Recommendation Engines For Retail with Neo4j Neo4j in Action
  12. 12. What are the Challenges from a Data Point of View in Retail Today?
  13. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 27. Case Studies
  28. 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. 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. 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. 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. 32. Towards Graph Inevitability
  33. 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. 34. “Forrester estimates that over 25% of enterprises will be using graph databases by 2017.” Towards Graph Inevitability
  35. 35. Valuable Resources! neo4jsandbox.com neo4j.com/industries/retail/ neo4j.com/product Sandbox Retail Solutions Product
  36. 36. Thank you!

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