From user preferences and location to time of day and weather, complex context representations have been the key to delivering personalised content. Graph databases excel at dealing with large amounts of complex data and therefore, they have been at the core of many modern real-time recommendation systems. In the near future, graph databases will play an equally important role in search personalisation. In this talk, Michal will introduce real-world content personalization requirements and show how Neo4j has been used to implement them. He will show how much recommendation and search engines have in common and give an overview of the latest innovations in the field of search personalisation.

1. and the Future of Search
GraphAware®
graphaware.com
@graph_aware
Real-Time
Recommendations
Michal Bachman

2. News you should read
Books you should buy
People you may know
People you should date
People you should market a product to
…
Recommendation Engines
GraphAware®

3. Clinical Trial Matching
Law Enforcement
Event Planning
Career Planning
…
Recommendation Engines
GraphAware®

4. Content-based (features)
Collaborative filtering (user <—> item relationships)
Recommendation Engines
GraphAware®

5. Features as well as relationships can be naturally
represented as a graph.
Good News
GraphAware®

6. Example
IS_OF_GENRE
title:
“Love Actually”
Movie
name: “Bob”
User
name:
“Comedy”
Genre
RATED
rating: 5
name: “Alice”
User
name:
“Romance”
Genre
title:
“American Pie”
Movie
IS_OF_GENRE
IS_OF_GENRE
RATEDrating: 5
INTERESTED_IN
rating: 5
RATED
GraphAware®

7. Traditional Approach
GraphAware®

8. Global view
Pre-computed (analytical)
Traditional Approach
GraphAware®

9. Bad Recommendation
GraphAware®

10. Bad Recommendation
GraphAware®

11. Global view
Pre-computed (analytical)
Lacks transparency
Inflexible
Lacks context
Traditional Approach
GraphAware®

12. Local view
Real-time (query)
Graph Approach
GraphAware®

13. InfoJobs
GraphAware®

14. Recommendation logic can be expressed as
graph traversals.
Graph Approach
GraphAware®

15. Local view
Real-time (query)
Flexible
Transparent
Contextual
Graph Approach
GraphAware®

16. Cypher Query Example
MATCH
(u:User {id:1})-[:LIKED]->(m:Movie),
(m)<-[:LIKED]-(another:User),
(another)-[:LIKED]->(reco:Movie)
WHERE NOT
(u)-[:LIKED|DISLIKED]->(reco)
RETURN reco;
GraphAware®

17. Requirements of real-world recommendation
engines are often much more complex.
The Reality
GraphAware®

18. Imagine you’re building the ”people you may
know” feature on LinkedIn.
Example
GraphAware®

19. After a brainstorming session, your team came up
with the following ways of finding people one may
know:
Example
GraphAware®

20. Common contacts
Facebook friends in common
Email / mobile contacts in common
Each others email / mobile contact
Worked for the same company
Studied at the same school
Share the same interest
Live in the same city
People You May Know
GraphAware®

21. But that’s just the beginning! Let’s go back and
re-visit.
Example
GraphAware®

22. More contacts in common = better chance?
Same city / school / company = does size matter?
What about emails that don’t represent a person?
What about people already connected?
And pending…
And rejected…
And repeatedly ignored…
People You May Know
GraphAware®

23. Recommendation Engines
GraphAware®

24. Discover
Score
Remove irrelevant
Measure quality
Recommendation Engines
GraphAware®

25. Discover
Score
Remove irrelevant
Measure quality
Recommendation Engines
GraphAware®

26. Discover
GraphAware®
IS_OF_GENRE
title:
“Love Actually”
Movie
name: “Bob”
User
name:
“Comedy”
Genre
RATED
rating: 5
name: “Alice”
User
name:
“Romance”
Genre
title:
“American Pie”
Movie
IS_OF_GENRE
IS_OF_GENRE
RATEDrating: 5
INTERESTED_IN
rating: 5
RATED

27. Discover
Score
Remove irrelevant
Measure quality
Recommendation Engines
GraphAware®

28. Score
GraphAware®
IS_OF_GENRE
title:
“Love Actually”
Movie
name: “Bob”
User
name:
“Comedy”
Genre
RATED
rating: 5
name: “Alice”
User
name:
“Romance”
Genre
title:
“American Pie”
Movie
IS_OF_GENRE
IS_OF_GENRE
RATEDrating: 5
INTERESTED_IN
rating: 5
RATED

29. Score
GraphAware®

30. Score
GraphAware®
LIVES_INMichal
age:31
London Daniela
age:25
LIVES_IN

31. Discover
Score
Remove irrelevant
Measure quality
Recommendation Engines
GraphAware®

32. Blacklist
Filter
Remove Irrelevant
GraphAware®

33. Remove Irrelevant
GraphAware®
IS_OF_GENRE
title:
“Love Actually”
Movie
name: “Bob”
User
name:
“Comedy”
Genre
RATED
rating: 5
name: “Alice”
User
name:
“Romance”
Genre
title:
“American Pie”
Movie
IS_OF_GENRE
IS_OF_GENRE
RATEDrating: 5
INTERESTED_IN
rating: 5
RATED

34. Discover
Score
Remove irrelevant
Measure quality
Recommendation Engines
GraphAware®

35. Measure Quality
GraphAware®
Recommendation: 33812631-079d-11e5
Input: 323423
Output: 99338
FriendsInCommon: 0.75 (raw value: 4)
SkillsInCommon: 0.50
SameLocation: 1.00
AgeDifference: -0.25
Total: 2.00
Outcome: 1

36. GraphAware Reco is an open-source recommendation
engine skeleton.
You Can Do It Too
GraphAware®

37. public class PeopleYouMayKnow extends Neo4jTopLevelDelegatingRecommendationEngine {
@Override
protected List<RecommendationEngine<Node, Node>> engines() {
return Arrays.<RecommendationEngine<Node, Node>>asList(
new SharedEmployment(),
new ConnectionsInCommon(),
new MobileContacts(),
new ReverseMobileContacts(),
new MobileContactsInCommon(),
new SharedStudies()
);
}
@Override
protected List<PostProcessor<Node, Node>> postProcessors() {
return Arrays.<PostProcessor<Node, Node>>asList(
new RewardSameCity(),
new RewardSameState(),
new PenalizeAlreadySeen()
);
}
@Override
protected List<BlacklistBuilder<Node, Node>> blacklistBuilders() {
return Arrays.asList(
new ExcludeSelf(),
new ExistingRelationshipBlacklistBuilder(CONNECTED, BOTH),
new ExistingRelationshipBlacklistBuilder(INVITED, OUTGOING),
new ExistingRelationshipBlacklistBuilder(REJECTED, BOTH)
);
}
@Override
protected List<Filter<Node, Node>> filters() {
return Arrays.<Filter<Node, Node>>asList(
new PrivacySettingTooHigh()
);
}
}
GraphAware®

38. plugin to Neo4j (uses GraphAware Framework)
you can use Cypher or a JVM-language
opinionated architecture
very fast
very flexible
handles all the plumbing
GraphAware Reco
GraphAware®

39. GraphAware®
What’s Next?

40. LinkedIn
GraphAware®

41. Has indexing
It’s getting better
There are more powerful tools
Neo4j
GraphAware®

42. Closer users on top
Products likely to buy on top
…
Requirements
GraphAware®
=> Recommendation Engine
with a constrained set of possible recommendations

43. Discover
Score
Remove irrelevant
Measure quality
Recommendation Engines
GraphAware®

44. Example
IS_OF_GENRE
title:
“Love Actually”
Movie
name: “Bob”
User
name:
“Comedy”
Genre
RATED
rating: 5
name: “Alice”
User
name:
“Romance”
Genre
title:
“American Pie”
Movie
IS_OF_GENRE
IS_OF_GENRE
RATEDrating: 5
INTERESTED_IN
rating: 5
RATED
GraphAware®

45. Graph-Aided Search
GraphAware®

46. Process of finding the most relevant results of a
search query by combining the relevancy scores
produced by a search engine with those produced
by a graph-based recommendation engine.
Graph-Aided Search
GraphAware®

47. Graph-Aided Search
GraphAware®
Application
Write
Replicate
(async)
OK

48. Graph-Aided Search
GraphAware®
Application
Results
Boost
Scores
Search

49. Graph-Aided Search
GraphAware®
Application
Write
Replicate
(async)
Results
BoostOK
Scores
Search
Recommendation Engine
Neo4j to Elasticsearch Elasticsearch to Neo4j

50. GraphAware®

51. GraphUnit
& RestTest
UUID WarmUp Auditing Importer
GraphAware Framework
GraphAware®
Reco Engine
Elasticsearch
Integration
TimeTree Algorithms NodeRank

52. Open Source (GPL)
Active
Production Ready
Github: github.com/graphaware
Our Web: graphaware.com
Maven Central
GraphAware Framework
GraphAware®

53. Try it
Give us feedback
Contribute
Build your own modules
Get in touch for support / consultancy
GraphAware Framework
GraphAware®

54. GraphAware®
www.graphaware.com
@graph_aware
GraphAware
the world’s #1 Neo4j consultancy