windy city db - recommendation engine with neo4j

Adding a Recommendation

EngineWindyCityDBMax De Marzi

About Me

• My Blog: http://maxdemarzi.com• Find me on Twitter: @maxdemarzi• Email me: maxdemarzi@gmail.com• GitHub: http://github.com/maxdemarzi

Built the Neography Gem (Ruby Wrapper to the Neo4j REST API)Playing with Neo4j since 10/2009

Agenda

• What is Neo4j?• What is Neography?• Approaches• Gremlin• Gremlin Recommends• Cypher• Cypher Recommends

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)

Obligatory CAP theorem Slide

• Neo4j is at the section joining Consistency with Availability just like your RDBMS

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)

Property Graph

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

What is Neography?

A very thin wrapper to the Neo4j REST API

• Two layers:Following the REST APIRuby 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

How do I install Neo4j?

Create a project directory and do this now if you haven’t yet. Make sure wget is installed.

Approaches

Collaborative Filtering

• Step 1: Collect User Behavior

• Step 2: Find similar Users

• Step 3: Recommend Behavior taken by similar users

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

Hybrid

• Combine the two for better results.

• Example: Netflix

What is Gremlin?

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

Marko Rodriguezhttp://markorodriguez.com

Created by:

A Graph DSL

A Dynamic Language for the JVM

A Data Flow Framework

“JDBC” for Graph DBs

Gremlin Recommends

Hybrid movie recommendations

Our Graph (from MovieLens)

Recommendation Algorithm

m = [:];x = [] as Set; v = g.v(node_id);

v. out('hasGenre').aggregate(x).back(2).inE('rated').filter{it. stars > 3}.

(continued)outV. outE('rated'). filter{it.stars > 3}.inV.filter{it != v}.filter{it.out('hasGenre').toSet().equals(x)}.groupCount(m){\"${it.id}:${it.title}\"}.iterate();m.sort{a,b -> b.value <=> a.value}[0..24]

Explanation

m = [:];x = [] as Set; v = g.v(node_id);

In Groovy [:] is a map, we will return thisThe set “x” will hold the collection of genres we want our recommendedmovies to have.

v is our starting point.

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.

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.

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 morethan 3 stars.

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.

Explanation

filter{it.out('hasGenre').toSet().equals(x)}.

We also want to keep only the movies that have the same genres as ourstarting movie. People gave Toy Story and Terminator both 4 stars,but you wouldn’t want to recommend one to the other.

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 kill30 minutes of your life trying to figure out why you get nothing.

Explanation

m.sort{a,b -> b.value <=> a.value}[0..24]

Finally, we sort our map by value in descending order and grab the top25 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.

What is Cypher?

The blue pill after the red pill

• Graph Query language for Neo4j• Based on Pattern Matching• Makes querying easy again

ASCII ART FTW: a--b, a-->b, a<--c

Cypher : Neo4j Query Language

Cypher Recommends

Similar UsersWhich users have rated movies that I have rated within one star of my rating?

Average RatingWhat is the average rating(by similar users) of a movie not rated by me?

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

Questions?

?

Thank you!http://maxdemarzi.com