This document summarizes a project that aims to develop semantic technologies to realize personalized and community-based TV experiences. It involves integrating broadcast and internet content and delivering personalized recommendations across devices. The project will demonstrate personalized semantic news, an enriched TV guide with adaptive advertising, and social internet TV. It focuses on collecting viewer attention data, deriving interests, and providing personalized recommendations of long-tail content across domains for multiple viewers.

2. FP7 Integrated Project
• Networks and ontologies for the transformation and
unification of broadcasting and the Internet
• Research
• Broadcast & Telecommunication
• Industry
• Dissemination & Training

3. 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

4. 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

5. demonstrators
• Personalized semantic
news
• Personalized TV guide
with adaptive
advertising
• Internet TV in the
Social Web

6. Personalized Semantic News

7. iFanzy: enriched Web TV guide
www.ifanzy.nl

8. iFanzy: enriched STB TV guide
www.ifanzy.nl

9. 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

10. 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

11. Bean counter & Recommender
• Bean Counter
– Attention data
aggregation
– Interest profile
generation
– Accessible via APIs
• Recommender
– Accessible via APIs
– Visualisation as EPG

12. explanations

13. 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

14. distributed personalization
• context-aware user modelling
– on different terminals
– recommendations of TV content
– personalized ads
• personalized presentation
– switching between devices
– multimedia content
– user profiles

16. 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

21. 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

22. http://notube.tv