NoTube: integrating TV and Web with the help of semantics

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NoTube: integrating TV and Web with the help of semantics

  1. 1. NoTube project overview
  2. 2. This talk • Project slogans • Architecture • Demonstrators: one example
  3. 3. Project slogans
  4. 4. “Putting the user back in the driver’s seat” • Personalized services are now common • But: user data is still under control of separate applications • Result: user is faced with multitude of distributed personal data, hidden in tons of inaccessible cookies
  5. 5. “TV is not bound to the device” • Multiple device scenarios – use of computer as TV & vice versa – use of mobile device as remote control  Architecture should support device independence
  6. 6. “Integration of TV & Web with help of semantics” • Integration of Web & TV is becoming a fact • Many practical software and hardware hurdles for users to handle this integration • Using semantics (linked open data) to open and interlink TV content in a Web fashion
  7. 7. Open standards • Focus on standards used by public broadcasters • Combined use of Web & TV standards • Where required: partial alignment of Web and TV standards
  8. 8. Out of scope • Content access issues – e.g geo‐restrictions on BBC content • TV content‐analysis research – video analysis – audio analysis • EPG metadata access issues • Privacy legislation
  9. 9. First demonstrator
  10. 10. Architecture
  11. 11. Building blocks: TV metadata services • EPG metadata grabbers – from 170+ channels • Issues – Web representation: e.g. channel URLs – metadata format  TV Anytime – real‐time service
  12. 12. Aside: Open Graph Protocol & RDFa
  13. 13. Building blocks: metadata enrichment • Making semantics in metadata explicit – e.g. channel name  channel URI • Add links to external Web vocabularies and repositories
  14. 14. Building blocks: user activity streams • Standard for activity stream representation – based on Open Social • Access services to activity streams – YouTube, Twitter, …. • Challenge – trusted access to “friend” information – implementation of OAuth 2.0 standard
  15. 15. Atom Activity Stream Model sample “verbs”
  16. 16. Building blocks: user profiling • Services for generating user preferences – “Beancounter” – abstractions from activity stream • User‐model representation based on FOAF
  17. 17. BEANCOUNTER aggregating user information
  18. 18. Building blocks: Recommenders • Collaborative recommenders – preferences of friends • Content‐based recommenders – program about Alma Mahler => program about Walter Gropius • Experiment with mix of these recommenders
  19. 19. Content recommendations: the role of patterns
  20. 20. Pattern occurrences in BBC dataset
  21. 21. Pattern examples
  22. 22. Hybrid recommendations
  23. 23. Architecture Overview T V P r o g r a m E n r i c h m e n t S e m a n t i c Pattern-based Recommendation Strategy RDFGraph TV Programs Semantic Content Patterns for TVPrograms H y b r id Recommendation Strategy S t a t i s t i c a l Similarity-based Recommendation Strategy User Ratings & Demographics (BBCEPG Data) EPGMetadata (BBC) R e c o m m e n d a t i o n S e r v ic e Similarity Clusters of Programs U s e r D a t a A n a ly s is End-UsersE n d U s e r s BEANCOUNTER
  24. 24. Sample demonstrator
  25. 25. N-Screen demonstrator
  26. 26. Suggestions for you
  27. 27. Drag-and-drop real-time sharing
  28. 28. More information about a programme
  29. 29. Changing the TV using drag and drop
  30. 30. Social TV survey
  31. 31. Some observations about Social TV • People want to watch TV together. They like talking about TV • People like others having watched the same thing as them, and older people miss the days when people were much more likely to have watched something they had. • To get the social benefit they don't have to watch it at the same time as others but sometimes this is fun.

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