Real-Time Recommendations and the Future of Search

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®

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

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

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

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

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