NoTube: Recommendations (Collaborative)
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NoTube: Recommendations (Collaborative) Presentation Transcript

  • 1. WP  3 User  profiling  &   Recommenda6on  (Part  3) BBC,  Pro-­‐ne+cs,  VUA 1Wednesday, March 28, 12
  • 2. Contents Overview User profiling General goal & approach From activity streams to profile Issues Analytics Beancounter Recommendations General goal & approach Semantic recommendation Statistical recommendation Hybrid recommendation Exploitation Conclusions 26-27 March 2012 NoTube 3rd Review 2Wednesday, March 28, 12
  • 3. Overview Semantic Content Semantic Patterns for Pattern-based TV Programs Recommendation EPG Metadata TV Program Strategy (BBC) Enrichment RDF Graph Statistical TV Recommendation Similarity-based Programs Service Recommendation Strategy User Ratings & Demographics User Data Similarity (BBC EPG Analysis Clusters Hybrid Data) of Programs Recommendation Strategy End End-Users Users 26-27 March 2012 NoTube 3rd Review 3Wednesday, March 28, 12
  • 4. Overview Semantic Content Semantic Patterns for Pattern-based TV Programs Recommendation EPG Metadata TV Program Strategy (BBC) Enrichment RDF Graph Statistical TV Recommendation Similarity-based Programs Service Recommendation Strategy User Ratings & Demographics User Data Similarity (BBC EPG Analysis Clusters Hybrid Data) of Programs Recommendation Strategy BEA NCO UNT E R End End-Users Users 26-27 March 2012 NoTube 3rd Review 3Wednesday, March 28, 12
  • 5. Statistical recommendations • We had privileged access to two bulk user ratings datasets from BBC • From these, used Apache Mahout toolkit to derive "item to item" similarity measures between each pair of items • With larger (20k users) this worked well; with a smaller (1k) dataset, less well • With BBC, investigating publication of these behaviour- derived similarity measures 26-27 March 2012 NoTube 3rd Review 4Wednesday, March 28, 12
  • 6. Hybrid models: factual paths and statistical similarity(and not to mention ‘@wossy’ is on Twitter with 1 million followers...) 31Wednesday, March 28, 12
  • 7. Statistical recommendation 12k 9 8 5 2 0 9 0 0 8 8 8 6 3 2 7 9 9 8 20k 26-27 March 2012 NoTube 3rd Review 6Wednesday, March 28, 12
  • 8. Statistical recommendation 9 0 0 0 0 9 0 0 8 0 8 0 0 0 7 0 9 8 26-27 March 2012 NoTube 3rd Review 7Wednesday, March 28, 12
  • 9. 9Wednesday, March 28, 12
  • 10. 10Wednesday, March 28, 12
  • 11. 11Wednesday, March 28, 12
  • 12. 12Wednesday, March 28, 12
  • 13. TV Preference Data is very sparse • Even for a single service (e.g. Netflix), data is ‘overwhelmingly sparse’ • For NoTube’s open systems, challenges multiply: – often no global view, only per-user data – many ways of identifying the same content item – many ways of identifying the same user – never mind other entities (actors, directors, ...) • Q: Can we tell a story about how organizations with such privileged overviews can contribute in a privacy respecting way to the public commons of linked data? (A: yes! see WP4) 26-27 March 2012 NoTube 3rd Review 12Wednesday, March 28, 12
  • 14. Fragmentation by site 26-27 March 2012 NoTube 3rd Review 13Wednesday, March 28, 12
  • 15. 29Wednesday, March 28, 12
  • 16. 30Wednesday, March 28, 12
  • 17. Statistical recommendation: Process • Build on best-in-class opensource code, rather than re- invent • Big-data ready (Hadoop-based) • Of various options, LogLikelihoodSimilarity generally gave best results (standard withold some ratings evaluation strategy) • Other explorations: including large scale (1/2 billion tweet) Twitter analysis, Spectral Clustering, using demographics, ... 26-27 March 2012 NoTube 3rd Review 16Wednesday, March 28, 12
  • 18. Exploitation & Further Development Beancounter: •Pronetics’ user profiling SaaS •integration in the e-commerce technological solution • making it more general purpose • making it capable of big data management a SaaS playground for Semantic Web researcher •open source licensing •community extensions 26-27 March 2012 NoTube 3rd Review 17Wednesday, March 28, 12
  • 19. Exploitation & Further Development Recommendations: •explore further the combination of demographic stereotypes & semantics in a hybrid approach to learn a prediction model for the shows a user is most likely interested in •integrate in personalized semantic search frameworks •extend with additional LOD sources •test further the measures for diversity, serendipity and predictability •open source licensing •community extensions 26-27 March 2012 NoTube 3rd Review 18Wednesday, March 28, 12
  • 20. Acknowledgements 26-27 March 2012 NoTube 3rd Review 19Wednesday, March 28, 12