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Reco4 @ Paris Meetup (May 20th)
- 1. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Reco4J Project Intelligent RecommendaDons for Your Business
- 2. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 1 Recommender Systems • A system that can recommend or present items to the user based on the user’s interests and interacDons • One of the best ways to provide a personalized customer experience • Built by exploiDng collecDve intelligence to perform predicDons • Examples: Amazon, YouTube, NeUlix, Yahoo, Tripadvisor, Last.fm, IMDb
- 3. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 2 The Example: NeUlix • The world largest online movie rental services, 33 million members in 40 countries • 60% of members selecDng movies based on recommendaDons (September 2008) • NeUlix Prize: US$ 1,000,000 was given to the BellKor's PragmaDc Chaos team which bested NeUlix's own algorithm for predicDng raDngs by 10.06% (September 2009) • 75% of the content watched on the service comes from its recommendaDon engine (April 2012)
- 4. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 3 Why Recommender Systems • Standard uses: – Increase the number of items sold – Sell more diverse items – Increase the user saDsfacDon – Increase user fidelity – Beger understand what the user wants • Advanced uses: – Create ad hoc campaigns (per geographic area, per type of users) – OpDmize products distribuDon over a wide area for large retail chains
- 5. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 4 Problem • There are no available sohware 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 soluDon" • There is no "one soluDon fits all” • The NeUlix winner composed 104 different algorithms
- 6. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 5 SoluDon: Reco4J A graph-‐based recommender engine
- 7. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 6 Reco4J Main Goals • Implement the state-‐of-‐the-‐art in the recommendaDon on top of a graph model • Provide sohware / cloud services / consultancy • Contribute to the RecSys research field
- 8. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 7 Reco4J Features • Composable models/algorithms • Persistent models (mulD model supported) • Updatable models • Commercial and research-‐oriented algorithms • Independent from source knowledge datasets • Cluster and cloud-‐ready • Context-‐aware recommendaDons • MulDtenant • Social recommendaDons
- 9. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 8 Reco4J Under the Hood • J is for Java • CollaboraDve 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 – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 9 Algorithms Roadmap • CollaboraDve filtering – Memory based (Neighborhood) • User/Item based – Several distance algorithms (Cosine, Euclidean, Tanimoto, etc.) • Graph based – Path Based Similarity (Shortest Path, Number of Paths) – Random Walk Similarity (Item Rank, Average first-‐passage/commute Dme) – Model based (Latent factor) • StochasDc gradient descendant • AlternaDng least square • Social recommendaDon – Trust based approach – ProbabilisDc approach
- 11. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 10 Algorithms Roadmap (2) • Crosswise Features – Context awareness – Composability – Real-‐Dme – ParallelizaDon
- 12. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 11 Context-‐Aware RecommendaDon “The ability to reach out and touch customers anywhere means that companies must deliver not just compe;;ve products but also unique, real-‐;me customer experiences shaped by customer context” C. K. Prahalad • Incorporate contextual informa5on in the recommendaDon process • Modeling contextual InformaDon – From: User x Item -‐> RaDng – To: User x Item x Context -‐> RaDng • Hierarchical structure • Three approaches – Contextual pre-‐filtering – Contextual post-‐filtering – Contextual modeling
- 13. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 12 Reco4J Context Model users items rate(c1, …, cn) …………. contextualized ra;ngs Context Tuple DAG usera-‐mp1 itemc-‐split1 …….. usera-‐mp2 itemc-‐split2 itemd-‐splitk userb-‐mpk …….. …….. user micro profiles item spliEng
- 14. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 13 Advantage of graph database • NoSQL database to handle BigData issue • Extensibility • No aggregate-‐oriented database • Minimal informaDon needed • Natural way for represenDng connecDons: – User -‐ to -‐ item – Item -‐ to -‐ item – User -‐ to -‐ User • Graph Based/Social Algorithms • Graph ParDDoning (sharding) • Performance
- 15. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 14 Example: Find Neighbors
- 16. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 15 Why Neo4J? • Java based • Embeddable/Extensible • NaDve graph storage with naDve graph processing engine • Open Source, with commercial version • Property Graph • ACID support • Scalability/HA • Comprehensive query/traversal opDons
- 17. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 16 RecommendaDon Model
- 18. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 17 Persistence Model
- 19. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 18 Persistence Model
- 20. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 19 Persistence Model
- 21. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 20 Reco4J + Hadoop • Queue Based Process • Operates both on cluster and cloud • Each process downloads data from Neo4J/Reco4J before or during computaDon • Stores data into Reco4J Model • Scaling augmenDng the number of: • Neo4J Nodes (only one master) • Hadoop Nodes
- 22. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 21 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
- 23. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 22 Consultancy Goals Analysis Data Source ExploraDon Process DefiniDon Import Data Test/ EvaluaDon Deploy
- 24. Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 23 Thank you Alessandro Negro Linkedin: hgp://it.linkedin.com/in/alessandronegro/ Email: alenegro81@gmail.com Luigi Giuri Linkedin: it.linkedin.com/in/luigigiuri Email: l.giuri@gmail.com Reco4J Site: hgp://www.reco4j.org Twiger: @reco4j GitHub: hgps://github.com/reco4j