This document discusses recommendation engines and provides examples using Gremlin and Cypher to generate recommendations in a graph database. It begins with an overview of collaborative filtering, content-based filtering, and hybrid recommendation approaches. It then demonstrates how to build recommendations using Gremlin to find similar users and recommend items those users liked. It also shows how to build recommendations using Cypher to find similar users and recommend uncviewed items highly rated by those similar users.

1. Adding a
Recommendation
Engine
WindyCityDB
Max De Marzi

2. About Me
Built the Neography Gem (Ruby
Wrapper to the Neo4j REST API)
Playing with Neo4j since 10/2009
• My Blog: http://maxdemarzi.com
• Find me on Twitter: @maxdemarzi
• Email me: maxdemarzi@gmail.com
• GitHub: http://github.com/maxdemarzi

3. Agenda
• What is Neo4j?
• What is Neography?
• Approaches
• Gremlin
• Gremlin Recommends
• Cypher
• Cypher Recommends

4. What is Neo4j?
• A Graph Database + Lucene Index
• Property Graph
• Full ACID (atomicity, consistency, isolation,
durability)
• High Availability (with Enterprise Edition)
• 32 Billion Nodes, 32 Billion Relationships,
64 Billion Properties
• Embedded Server (Java or JVM languages)
• REST API (for everyone else)

5. Obligatory CAP theorem Slide
• Neo4j is at the section
joining Consistency
with Availability just
like your RDBMS

6. Good For
• Highly connected data (social networks)
• Recommendations (e-commerce)
• Path Finding (how do I know you?)
• A* (Least Cost path)
• Data First Schema (bottom-up, but you still
need to design)

7. Property Graph

8. If you’ve ever
• Joined more than 7 tables together
• Modeled a graph in a table
• Written a recursive CTE
• Tried to write some crazy stored procedure
with multiple recursive self and inner joins
You should use Neo4j

9. What is Neography?

10. A very thin wrapper to
the Neo4j REST API
• Two layers:
Following the REST API
Ruby Sugar
• Made for graph database programmer happiness
>8-]
• Read the github Readme and Specs
• Can I haz pull requests?
• Want Active Record?
Use the activerecord-neo4j-adapter gem which
sits on top of neography

11. How do I install Neo4j?
Create a project directory and
do this now if you haven’t yet.
Make sure wget is installed.

12. Approaches

13. Collaborative Filtering
• Step 1: Collect User
Behavior
• Step 2: Find similar
Users
• Step 3: Recommend
Behavior taken by
similar users

14. Content Based Filtering
• Step 1: Collect Item
Characteristics
• Step 2: Find similar
Items
• Step 3: Recommend
Similar Items
Marko likes Romantic Zombie Comedies,
what other romantic zombie comedies are there?
Tweet him @twarko

15. Hybrid
• Combine the two for
better results.
• Example: Netflix

16. What is Gremlin?

17. Gremlin is
• A Graph Traversal Language
• A domain specific language for traversing
property graphs
• Implemented by most Graph Database
Vendors
• Primarily seen with the Groovy Language
• With JVM connectivity in Java, Scala, and
other languages

18. Created by:
Marko Rodriguez
http://markorodriguez.com

19. A Graph DSL
A Dynamic Language for the JVM
A Data Flow Framework
“JDBC” for Graph DBs

20. Gremlin Recommends

21. Hybrid movie recommendations

22. Our Graph (from MovieLens)

23. Recommendation Algorithm
m = [:];
x = [] as Set; (continued)
v = g.v(node_id); outV.
outE('rated').
v. filter{it.stars > 3}.
out('hasGenre'). inV.
aggregate(x). filter{it != v}.
back(2). filter{it.out('hasGenre').toSet().equals(x)}.
inE('rated'). groupCount(m){"${it.id}:${it.title}"}.iterate();
filter{it. stars > 3}. m.sort{a,b -> b.value <=> a.value}[0..24]

24. Explanation
m = [:];
x = [] as Set;
v = g.v(node_id);
In Groovy [:] is a map, we will return this
The set “x” will hold the collection of genres we want our recommended
movies to have.
v is our starting point.

25. Explanation
v.
out('hasGenre'). (we are now at a genre node)
aggregate(x).
We fill the empty set “x” with the genres of our movie.
These are the properties we want to make sure our recommendations have.

26. Explanation
back(2). (we are back to our starting point)
inE('rated').
filter{it. stars > 3}. (we are now at the link between our movie and users)
We go back two steps to our starting movie, go to the relationship ‘rated’
and filter it so we only keep those with more than 3 stars.

27. Explanation
outV. (we are now at a user node)
outE('rated').
filter{it.stars > 3}. (we are now at the link between user and movie)
We follow our relationships to the users who made them, and then
go to the “rated” relationships of movies which also received more
than 3 stars.

28. Explanation
inV. (we are now at a movie node)
filter{it != v}.
We follow our relationships to the movies who received the, but filter out “v”
which is our starting movie. We do not want the system to recommend the
same movie we just watched.

29. Explanation
filter{it.out('hasGenre').toSet().equals(x)}.
We also want to keep only the movies that have the same genres as our
starting movie. People gave Toy Story and Terminator both 4 stars,
but you wouldn’t want to recommend one to the other.

30. Explanation
groupCount(m){"${it.id}:${it.title}"}.iterate();
groupCount does what it sounds like and stores the values in the map “m”
we created earlier, but we to retain the id and title of the movies.
iterate() is needed from the Neo4j REST API, the gremlin shell does
it automatically for you. You will forget this one day and kill
30 minutes of your life trying to figure out why you get nothing.

31. Explanation
m.sort{a,b -> b.value <=> a.value}[0..24]
Finally, we sort our map by value in descending order and grab the top
25 items… and you’re done.
See http://maxdemarzi.com/2012/01/16/neo4j-on-heroku-part-two/
for the full walk-through including data loading.

32. What is Cypher?

33. The blue pill after the red pill
• Graph Query language for Neo4j
• Based on Pattern Matching
• Makes querying easy again

34. Cypher : Neo4j Query Language
ASCII ART FTW: a--b, a-->b, a<--c

35. Cypher Recommends

36. Similar Users
Which users have rated movies that I have rated within one star of my rating?

37. Average Rating
What is the average rating(by similar users) of a movie not rated by me?

38. Movies I should see
Which movies (that I haven't seen) have been rated 4 stars or higher
by users similar to me?

39. Questions?
?

40. Thank you!
http://maxdemarzi.com