A tutorial at the PyData London conference on building a recommendation engine using Python and neo4j.

1. Build a recommendation engine
with Python and Neo4j
Mark Needham / @markhneedham

2. Logistics
‣ Download and install Neo4j 3.0.0
• http://neo4j.com/download/
‣ Open your browser to http://localhost:7474

3. Introducing our data set...

4. meetup.com’s recommendations

5. Recommendation queries
‣ Several different types
• groups to join
• topics to follow
• events to attend
‣ As a user of meetup.com trying to find
groups to join and events to attend

6. The data
meetup.com/meetup_api/

9. What data do we have?
‣ Groups
‣ Members
‣ Events
‣ Topics
‣ Time & Date
‣ Location

10. Find similar groups to Neo4j
As a member of the Neo4j London group
I want to find other similar meetup groups
So that I can join those groups

11. What makes groups similar?

12. Find similar groups to Neo4j
As a member of the Neo4j London group
I want to find other similar meetup groups
So that I can join those groups

13. Nodes
As a member of the Neo4j London group
I want to find other similar meetup groups
So that I can join those groups

14. Relationships
As a member of the Neo4j London group
I want to find other similar meetup groups
So that I can join those groups

15. Labels
As a member of the Neo4j London group
I want to find other similar meetup groups
So that I can join those groups

16. Properties
As a member of the Neo4j London group
I want to find other similar meetup groups
So that I can join those groups

17. ‣ Open your browser to http://localhost:7474 if
you haven’t already
‣ Type the following command:
:play http://guides.neo4j.com/pydata
Recommend groups by topic

18. When there are slides to see

19. Follow Along
Let’s go through the first guide

20. Take Note
Indexes

21. Indexes
We create indexes to:
‣ allow fast lookup of nodes which match
these (label,property) pairs.

22. Indexes
We create indexes to:
‣ allow fast lookup of nodes which match
these (label,property) pairs.
CREATE INDEX ON :Group(name)

23. The following are index backed:
‣ Equality
‣ STARTS WITH
‣ CONTAINS,
‣ ENDS WITH
‣ Range searches
‣ (Non-)existence checks
Indexes

24. How does Neo4j use indexes?
Indexes are only used to find the starting
point for queries.
Use index scans to look up
rows in tables and join them
with rows from other tables
Use indexes to find the starting
points for a query.
Relational
Graph

25. Continue
Continue with the Guide

26. Exercise
Explore the Graph

27. Solution
Explore the Graph

28. Continue
Continue with the Guide

29. Next Step
Clustering topics

30. Next Step
Group Membership

31. Exclude groups I’m a member of
As a member of the Neo4j London group
I want to find other similar meetup groups
that I’m not already a member of
So that I can join those groups

32. Group memberships

33. Next Guide
Group Membership

34. Watch Out
Transactions & WITH

35. Periodic Commit
Cypher keeps all transaction state in memory
while running a query which is fine most of the
time.

36. Periodic Commit
Cypher keeps all transaction state in memory
while running a query which is fine most of the
time.
But when using LOAD CSV, this state can get
very large and may result in an OutOfMemory
exception.

37. Periodic Commit
// defaults to 1000
USING PERIODIC COMMIT
LOAD CSV
...

38. Periodic Commit
// defaults to 1000
USING PERIODIC COMMIT 10000
LOAD CSV
...

39. WITH
The WITH clause allows query parts to be chained
together, piping the results from one to be used
as starting points or criteria in the next.

40. WITH
It’s used to:
‣ limit the number of entries that are then
passed on to other MATCH clauses.
‣ filter on aggregated values
‣ separate reading from updating of the graph

41. Continue
Continue with the Guide

42. Exercise
Find yourself and your groups

43. Solution
Find yourself and your groups

44. Continue
Continue with the Guide

45. Next Guide
Event recommendations

46. Exercise
Extend event recommendations

47. Solution
Extend event recommendations

48. That’s all for today!
Mark Needham / @markhneedham