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Reco4J @ Munich Meetup (April 18th)
- 1. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Reco4J Project Intelligent RecommendaAons for Your Business
- 2. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 1 Recommender Systems • A system that can recommend or present items to the user based on the user’s interests and interacAons • One of the best ways to provide a personalized customer experience • Built by exploiAng collecAve intelligence to perform predicAons • Examples: Amazon, YouTube, NeRlix, Yahoo, Tripadvisor, Last.fm, IMDb
- 3. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 2 The Example: NeRlix • The world largest online movie rental services, 33 million members in 40 countries • 60% of members selecAng movies based on recommendaAons (September 2008) • NeRlix Prize: US$ 1,000,000 was given to the BellKor's PragmaAc Chaos team which bested NeRlix's own algorithm for predicAng raAngs by 10.06% (September 2009) • 75% of the content watched on the service comes from its recommendaAon engine (April 2012)
- 4. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 3 Why Recommender Systems • Standard uses: – Increase the number of items sold – Sell more diverse items – Increase the user saAsfacAon – Increase user fidelity – Beeer understand what the user wants • Advanced uses: – Create ad hoc campaigns (per geographic area, per type of users) – OpAmize products distribuAon over a wide area for large retail chains
- 5. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 4 Problem • There are no available sofware products for state-‐of-‐ the-‐art recommender systems • A high-‐end recommender engine can be built only through expensive custom projects • Large scale user/item datasets require a big data approach • There is no "best soluAon" • There is no "one soluAon fits all” • The NeRlix winner composed 104 different algorithms
- 6. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 5 SoluAon: Reco4J A graph-‐based recommender engine
- 7. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 6 Reco4J Main Goals • Implement the state-‐of-‐the-‐art in the recommendaAon on top of a graph model • Provide sofware / cloud services / consultancy • Contribute to the RecSys research field
- 8. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 7 Reco4J Features • Composable models/algorithms • Persistent models • Updatable Models • Independent from source knowledge datasets • Cluster and cloud-‐ready • MulAtenant • Social recommendaAons
- 9. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 8 Reco4J Under the Hood • J is for Java • CollaboraAve filtering algorithms – Neighborhood-‐based methods – Latent factor models • Neo4J Graph Database: – Data source repository – Persistent model repository • Hadoop cluster/MapReduce • Apache Mahout
- 10. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 9 Advantage of graph database • NoSQL database to handle BigData issue • Extensibility • No aggregate-‐oriented database • Minimal informaAon needed • Natural way for represenAng connecAons: – User -‐ to -‐ item – Item -‐ to -‐ item – User -‐ to -‐ User • Graph ParAAoning (sharding) • Performance
- 11. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 10 Example: Find neighbors
- 12. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 11 Why Neo4J? • Java based • Embeddable/Extensible • NaAve graph storage with naAve graph processing engine • Open Source, with commercial version • Property Graph • ACID support • Scalability/HA • Comprehensive query/traversal opAons
- 13. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 12 RecommendaAon Model
- 14. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 13 Persistence Model
- 15. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 14 Persistence Model
- 16. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 15 Persistence Model
- 17. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 16 Reco4J + Hadoop • Queue Based Process • Operates both on cluster and cloud • Each process downloads data from Neo4J/Reco4J before or during computaAon • Stores data into Reco4J Model • Scaling augmenAng the number of: • Neo4J Nodes (only one master) • Hadoop Nodes
- 18. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 17 Reco4J in the Cloud • Recommenda)on as a service (RaaS) • Reco4J cloud infrastructure offers: – Pay as you need – Pay as you grow – Support for burst – Periodical analysis at lower costs – Test/evaluate several algorithms on a reduced dataset – Compose algorithms dynamically
- 19. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 18 Consultancy Goals Analysis Data Source ExploraAon Process DefiniAon Import Data Test/ EvaluaAon Deploy
- 20. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 19 Research Topics • Real-‐Time recommendaAon • MulA-‐criteria recommender systems • Recommending to groups • Parallel algorithms • Filtering
- 21. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 20 Reco4J Site AnalyAcs
- 22. Alessandro Negro Reco4J Project @ Munich Meetup -‐ April 2013 Page 21 Thank you Alessandro Negro Linkedin: hep://it.linkedin.com/in/alessandronegro/ Email: alenegro81@gmail.com Reco4J Site: hep://www.reco4j.org Twieer: @reco4j GitHub: heps://github.com/reco4j