FP7 Integrated Project

• Networks and ontologies for the transformation and
  unification of broadcasting and the Interne...
getting personal with your TV
• Semantic technology to realize new level of
  service for context-dependent and personaliz...
who will benefit
• viewers: gain control over TV-entertainment;
  classical mass-media broadcasting changes by
  users‘ So...
demonstrators
        • Personalized semantic
          news

        • Personalized TV guide
          with adaptive
    ...
Personalized Semantic News
iFanzy: enriched Web TV guide




                        www.ifanzy.nl
iFanzy: enriched STB TV guide




                        www.ifanzy.nl
Internet TV in Social Web




                           Friends following this event
                           Friends f...
Internet TV in Social Web




                               Friends following this event
        Tom
        Billy
      ...
Bean counter & Recommender
                   • Bean Counter
                      – Attention data
                      ...
explanations
Web-service-based metadata
           exchange components
• vocabulary alignment, semantic annotation
   – ClioPatria, htt...
distributed personalization
• context-aware user modelling
  – on different terminals
  – recommendations of TV content
  ...
personalization focus
•   Collection of attention data
•   Derive interests from attention data
•   Surface programs of in...
Acknowledgements
•   Chris van Aart   •   Lyndon Nixon
•   Dan Brickley     •   Davide Palmisano
•   Stefan Dietze    •   ...
http://notube.tv
SemTech09 European Day: presentation by NoTube
SemTech09 European Day: presentation by NoTube
SemTech09 European Day: presentation by NoTube
SemTech09 European Day: presentation by NoTube
SemTech09 European Day: presentation by NoTube
SemTech09 European Day: presentation by NoTube
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SemTech09 European Day: presentation by NoTube

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SemTech09 European Day: presentation by NoTube

  1. 1. FP7 Integrated Project • Networks and ontologies for the transformation and unification of broadcasting and the Internet • Research • Broadcast & Telecommunication • Industry • Dissemination & Training
  2. 2. getting personal with your TV • Semantic technology to realize new level of service for context-dependent and personalized selection of TV content • Community to bring content consumption from a single-user activity to a community-based experience • Distributed personalization in distributed, interactive and multi-device environment
  3. 3. who will benefit • viewers: gain control over TV-entertainment; classical mass-media broadcasting changes by users‘ Social Web engagement • providers: information integration in a TV- Internet environment for personalized content delivery • advertisers: a contextualized and personalized TV-based narrowcasting
  4. 4. demonstrators • Personalized semantic news • Personalized TV guide with adaptive advertising • Internet TV in the Social Web
  5. 5. Personalized Semantic News
  6. 6. iFanzy: enriched Web TV guide www.ifanzy.nl
  7. 7. iFanzy: enriched STB TV guide www.ifanzy.nl
  8. 8. Internet TV in Social Web Friends following this event Friends following this event Billy That was never a corner.. Friends following this event Friends following this event
  9. 9. Internet TV in Social Web Friends following this event Tom Billy That was never a corner.. Full screen Friends following this event Friends following this event
  10. 10. Bean counter & Recommender • Bean Counter – Attention data aggregation – Interest profile generation – Accessible via APIs • Recommender – Accessible via APIs – Visualisation as EPG
  11. 11. explanations
  12. 12. Web-service-based metadata exchange components • vocabulary alignment, semantic annotation – ClioPatria, http://e-culture.multimedian.nl/software/ClioPatria.shtml – NoTube vocabularies (few examples): – VU event ontology, BBC event ontology – OntoMedia Core, FOAF, Web ofTtrust – wsml.rdf wsmo-lite.rdf wsmo.rdf – IMDB, Music ontology, Programs ontology – dbpedia, WordNet – W3C time ontology
  13. 13. distributed personalization • context-aware user modelling – on different terminals – recommendations of TV content – personalized ads • personalized presentation – switching between devices – multimedia content – user profiles
  14. 14. personalization focus • Collection of attention data • Derive interests from attention data • Surface programs of interest in the ‘long tail’ • Cross-domain recommendations • Multi-person recommending • Granular control for users
  15. 15. Acknowledgements • Chris van Aart • Lyndon Nixon • Dan Brickley • Davide Palmisano • Stefan Dietze • Yves Raimond • Eylem Kehribar • Guus Schreiber • HyoMin Kim • Ronald Siebes • Libby Miller • Jan Wielemaker • Michele Minno • … other NoTube partners
  16. 16. http://notube.tv

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