The document discusses the creation of a recommendation engine using Neo4j and reco4php, addressing challenges like finding and serving relevant recommendations. It includes an overview of graph databases and technical challenges such as performance and simplicity. Additionally, it presents a practical demonstration of building a simple recommendation engine for GitHub repositories.

1. Your own
Recommendation
Engine
with Neo4j and Reco4PHP

2. About me
• Christophe Willemsen
• Neo4j & Elasticsearch Consultant @ GraphAware
• Belgium
@ikwattro

3. What we’ll talk about
• Challenges of a recommendation engine
• Quick intro to graph databases (Neo4j)
• Reco4PHP
• Build your own (demo)

4. Challenges
of a
Recommendation
Engine

5. Recommendations : Overview
• News you should read (blogs)
• Books you should buy (e-commerce)
• Persons you may know (social)
• Which talk to attend (knowledge)
• Which service is best suited to handle a given failure (impact analysis)
• Finding the right candidates for medical proofs (healthcare)
• …

6. Main types of recommendations
• Content based (features)
• Collaborative filtering (relations user <-> item, knn, matrix factorization)
• Knowledge based

7. Combination of those by completely crazy scientists + secret sauce

8. Business Challenges
• Finding things to recommend
• Serve the most relevant recommendations
• Measuring the quality of recommendations
• Time to market / cost of development

9. Business challenges
Imagine that you should implement
the feature
“People you may know”
on LinkedIn

10. Business challenges : Finding candidates
• Common contacts
• Facebook friends in common
• Mobile/email contacts in common
• All contacts of your contacts
• Worked for same company
• Studied at same school
• Share same interests
• Lives in same city

11. Business challenges
It’s just the beginning !
Let’s revisit …

12. Business challenges : most relevant
More contacts in common:
More relevant ?

13. Business challenges : most relevant
Same city, worked for same company,
studied at same school

15. Business challenges : most relevant
What to do with emails that
actually don’t represent a person?
Should we recommend info@coolblue.nl?

16. Business challenges : most relevant
What should we do with contacts
pending?
rejected?
always ignored?

17. Technical Challenges
• Simplicity
• Performance (Real-time or near real-time)
• Flexibility

18. Intro to
Graph Databases
( Neo4j )

20. is a
graph database.

21. Nodes

22. A node can have one or multiple labels
Person, Male

23. A node can one or more properties
Person, Male
name : Mark

24. Nodes are connected by relationships

25. Relationships have a type

26. Relationships have a direction

27. Relationships can have properties

29. Cypher : the query language for graphs

30. Declarative pattern matching language
Describe
what you want
to retrieve from the database,
not
how to get it.

31. Declarative pattern matching language
MATCH (you:Person {name:”You”})-[:ATTENDED]->(talk),
(talk)<-[:ATTENDED]-(other)
WHERE (you)-[:LIVES_IN]->(city)<-[:LIVES_IN]-(other)
AND you <> other
RETURN other AS maybeFriend

32. Why graphs ?

33. Technical Challenges
• Simplicity
• Performance (Real-time or near real-time)
• Flexibility

35. Simplicity
Features as well as relationshipsare
naturally represented in
a graph database.

36. Simplicity
The recommendation logic can be
represented in a graph traversal.

39. Technical Challenges
• Simplicity
• Performance (Real-time)
• Flexibility

40. Performance
• Relationships are first class citizens ( no joins ! )
• Traversing relationships in Neo4j is cheap
( 4 millions traversals per second per core )

41. Reco4PHP
Open Source
Recommendation Engine
Skeleton
atop Neo4j

42. Reco4PHP
• Standalone PHP library
• Opinionated architecture
• Flexible
• Handle all the glue

43. Architecture
• Input -> Discovery Engines -> Recommendations
• Scores and Score builders
• Blacklists and Filters
• Post Processors
• Context (how many, meteo, all k/v external factors..)

44. Design decisions
• DiscoveryEngine per recommendation “reason” (core logic)
• DiscoveryEngine executes a graph traversal
(Cypher query)
• Items discovered multiple times are more relevant

45. Technical Challenges
• Simplicity
• Performance (Real-time or near real-time)
• Flexibility

46. Let’s build one
In 5 minutes, we’ll build a simple engine that
recommends repositories you might
be interested in on Github.

47. composer require graphaware/reco4php

48. 0. The Model
:STARS
:CONTRIBUTED_TO
:OWNS
:FOLLOWS
:FOLLOWS
:WRITTEN_IN

49. 1. Discover
Find repositories starred by the people you follow or following
you.

51. 2. Serve only relevant recommendations
We’ll create a blacklist that will exclude the repositories I am
the owner of.

53. 3. Post process
We’ll reward the recommended repositories by let’s say 5
points if they are written in PHP.

55. 4. Assemble

57. 5. Tadaaaaaam

59. Thank you !
Questions ?
graphaware.com
github.com/graphaware
@ikwattro