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![Cypher Example
CREATE (n {name: {value}})
CREATE (n)-[r:KNOWS]->(m)
START
[MATCH]
[WHERE]
RETURN [ORDER BY] [SKIP] [LIMIT]](https://image.slidesharecdn.com/neo4j-graphdatabaseforrecommendations-130531021030-phpapp02/85/Neo4j-graph-database-for-recommendations-7-320.jpg)
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Neo4j - graph database for recommendations
This document discusses using Neo4j, a graph database, for recommendations. It describes modeling data as graphs in Neo4j and developing recommendation algorithms and plugins for it, such as for document similarity, movie recommendations, and restricting recommendations to a subgraph. An example application called TeleVido.tv is also mentioned that provides media content recommendations using Neo4j.
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Similar to Neo4j - graph database for recommendations
Neo4j - graph database for recommendations
- 1. Neo4j - Graphdatabase for recommendations Jakub Kříž, Ondrej Proksa30.5.2013
- 2. Summary Graph databases Working with Neo4j and Ruby (On Rails) Plugins and algorithms – live demos Document similarity Movie recommendation Recommendation from subgraph TeleVido.tv
- 3. Why Graphs? Graphsare everywhere! Natural way to model almost everything “Whiteboard friendly” Even the internet is a graph
- 4. Why Graph Databases? Relational databases are not so great for storing graph structures Unnatural m:n relations Expensive joins Expensive look ups during graph traversals Graph databases fix this Efficient storage Direct pointers = no joins
- 5. Neo4j The World'sLeading Graph Database www.neo4j.org NOSQL database Open source - github.com/neo4j ACID Brief history Official v1.0 – 2010 Current version 1.9 2.0 coming soon
- 6. Querying Neo4j Queryinglanguages Structurally similar to SQL Based on graph traversal Most often used Gremlin – generic graph querying language Cypher – graph querying language for Neo4j SPARQL – generic querying language for data in RDF format
- 7. Cypher Example CREATE (n{name: {value}}) CREATE (n)-[r:KNOWS]->(m) START [MATCH] [WHERE] RETURN [ORDER BY] [SKIP] [LIMIT]
- 8. Cypher Example (2) Friend of a friend START n=node(0) MATCH (n)--()--(f) RETURN f
- 9. Working with Neo4j REST API => wrappers Neography for Ruby py2neo for Python … Your own wrapper Java API Direct access in JVM based applications neo4j.rb
- 10. Neography – APIwrapper example # create nodes and properties n1 = Neography::Node.create("age" => 31, "name" => "Max") n2 = Neography::Node.create("age" => 33, "name" => "Roel") n1.weight = 190 # create relationships new_rel = Neography::Relationship.create(:coding_buddies, n1, n2) n1.outgoing(:coding_buddies) << n2 # get nodes related by outgoing friends relationship n1.outgoing(:friends) # get n1 and nodes related by friends and friends of friends n1.outgoing(:friends).depth(2).include_start_node
- 11. Neo4j.rb – JRubygem example class Person < Neo4j::Rails::Model property :name property :age, :index => :exact # :fulltext has_n(:friends).to(Person).relationship(Friend) end class Friend < Neo4j::Rails::Relationship property :as end mike = Person.new(:name => ‘Mike’, :age => 24) john = Person.new(:name => ‘John’, :age => 27) mike.friends << john mike.save
- 12. Our Approach Relationaldatabases are not so bad Good for basic data storage Widely used for web applications Well supported in Rails via ActiveRecord Performance issues with Neo4j However, we need a graph database We model the domain as a graph Our recommendation is based on graph traversal
- 13. Our Approach (2) Hybrid model using both MySQL and Neo4j MySQL contains basic information about entities Neo4j contains only relationships Paired via identifiers (neo4j_id)
- 14. Our Approach (3) Recommendation algorithms Made as plugins to Neo4j Written in Java Embedded into Neo4j API Rails application uses custom made wrapper Creates and modifies nodes and relationships via API calls Handles recommendation requests
- 15. Graph Algorithms Built-inalgorithms Shortest path All shortest paths Dijkstra’s algorithm Custom algorithms Depth first search Breadth first search Spreading activation Flows, pairing, etc.
- 16. Document Similarity Task:find similarities between documents Documents data model: Each document is made of sentences Each sentence can be divided into n-grams N-grams are connected with relationships Neo4J is graph database in Java (Neo4j, graph) – (graph, database) – (database, Java)
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- 18. Detecting similardocuments in our graph model Shortest path between documents Number of paths shorter than some distance Weighing relationships How about a custom plugin? Spreading activation Document Similarity (3)
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- 20. Task: recommendmovies based on what we like We like some entities, let’s call them initial Movies People (actors, directors etc.) Genres We want recommended nodes from input Find nodes which are The closest to initial nodes The most relevant to initial nodes Movie Recommendation
- 21. 165k nodes Movies People Genre 870k relationships Movies – People Movies – Genres Easy to add more entities Tags, mood, period, etc. Will it be fast? We need 1-2 seconds Movie Recommendation (2)
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- 23. Breadth firstsearch Union Colors Mixing Colors Modified Dijkstra Weighted relationships between entities Spreading activation (energy) Each initial node gets same starting energy Recommendation Algorithms
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- 31. Experimental evaluation Which algorithm is the best (rating on scale 1-5) 30 users / 168 scenarios Recommendation - Evaluation 0 0.5 1 1.5 2 2.5 3 3.5 Spájanie farieb Miešanie farieb Šírenie energie Dijkstra
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- 33. Movie Recommendation –User Model Spreading energy Each initial node gets different starting energy Based on user’s interests and feedback Improves the recommendation!
- 34. Recommendation from subgraph Recommend movies which are currently in cinemas Recommend movies which are currently on TV How? Algorithm will traverse normally Creates a subgraph from which it returns nodes
- 35. Live Demo… Recommendation fromsubgraph (2)
- 36. TeleVido.tv Media contentrecommendation using Neo4j Movie recommendation Recommendation of movies in cinemas Recommendation of TV programs and schedules
- 37. Summary Graph databases Working with Neo4j and Ruby (On Rails) Plugins and algorithms Document similarity Movie recommendation Recommendation from subgraph TeleVido.tv