1. The NoTube BeanCounter
Aggregating User Data for Television
Programme Recommendation
Chris van Aart1, Lora Aroyo1, Dan Brickley13, Vicky Buser2, Libby Miller2,
Michele Minno3, Michele Mostarda3, Davide Palmisano3, Yves Raimond2,
Guus Schreiber1, and Ronald Siebes1
1: VU University Amsterdam, the Netherlands
2: BBC - Future Media & Technology, London, UK
3: Asemantics Srl, Rome Italy
SDoW2009@ISWC, Washington, Oct 25, 2009

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

3. Objectives

4. Killer Applications
• Personalized semantic
news
• Personalized TV guide
with adaptive advertising
• Internet TV in the Social
Web

5. Programmes lifecycle

6. Television and Social Web
Friends following this event
Friends following this event
Billy
That was
never a
corner..
Friends following this event
Friends following this event

7. 7
Hyper ego
FredCavazza.net.

8. Television and Social Web

9. Enrichment

11. Vocabularies
• event.rdf (VU event ontology, BBC event
ontology), expression.rdf (OntoMedia), foaf.rdf,
wot.rdf, imdb.rdf ,, po.rdf
• UserContext, Eventlog (zapper user log)
• hrests.rdf, wsml.rdf wsmo-lite.rdf wsmo.rdf
• mo.rdf, relationship.rdf,rev.rdf skos.rdf
• Dbpedia, WordNet
• W3C time ontology

12. Trend analysis: counting
• Concepts (What categories of programme do you
and your friends like?)
• Series (What series have you watched and your
friends the most?)
• Location / context (Where do you and your friends
usually watch TV?)
• Time periods (When do you and your friends usually
watch?)
• Mood (how did you and your friends feel?)

13. The BeanCounter

15. Trivial recommendations
series I like
series my friends like
Broadcast and on‐demand availability
data
Dr. Who is on tomorrow at 23:20

16. Recommendation Explanations

17. ProtoType

18. Inference

19. Inference

20. bbc.co.uk (po)
http://www.bbc.co.uk/programmes/b00mqjhr.rdf
http://www.bbc.co.uk/bbcone/programmes/schedules/london.xml

21. Inference

22. IMDB enrichment
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:imdbs="http://www.csd.abdn.ac.uk/~ggrimnes/dev/imdb/IMDB#"
xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:foaf="http://xmlns.com/foaf/0.1/">
<rdf:Description rdf:about="http://www.imdb.com/title/tt0147926">
<rdf:type rdf:resource="http://www.csd.abdn.ac.uk/~ggrimnes/dev/imdb/IMDB#Movie"></rdf:type>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0712391"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0646818"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0005156"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0821048"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0434079"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0586994"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0277882"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0864997"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0000396"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0641934"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0001431"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0875768"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0949385"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0853122"></imdbs:Actor>
<imdbs:Actor rdf:resource="http://www.imdb.com/person/nm0236519"></imdbs:Actor>
<imdbs:Director rdf:resource="http://www.imdb.com/person/"></imdbs:Director>
</rdf:Description>
<rdf:Description rdf:about="http://www.bbc.co.uk/programmes/b008yk93">
<skos:related rdf:resource="http://www.imdb.com/title/tt0147926"></skos:related>
<rdf:type rdf:resource="http://purl.org/ontology/po/Programme"></rdf:type>
</rdf:Description>
<rdf:Description rdf:about="http://www.imdb.com/person/nm0712391">
<rdf:type rdf:resource="http://www.csd.abdn.ac.uk/~ggrimnes/dev/imdb/IMDB#Actor"></rdf:type>
<foaf:name>Thurl Ravenscroft</foaf:name>
<foaf:image></foaf:image>
</rdf:Description>

23. Aligning Vocabularies
• Alignment of Genre vocabularies
– manual, small number
– xmltv:documentaire  tva:documentary
– imdb:thriller  tva:thriller
– imdb:sci-fi  tva:science_fiction

24. Enriching Vocabularies
• Semantic enrichment of Genre vocabulary
– tva:news – skos:narrower  tva:sport_news => Original
XML Term hierarchy
– tva:sport_news – skos:related  tva:sport => Partial label
matches
– tva:american_football – skos:related  tva:rugby =>
Siblings background knowl.
• Semantic enrichment of TV metadata with
IMDB movie descriptions
– “Buono, il brutto, il cattivo, Il (1966)”  “The Good, the
Bad and the Ugly” based on IMDB country AKA-titles

25. Inference

26. EPG; content + actions

27. Inference

28. iZapper

29. Viewerlog

30. Inference

31. User profile

32. Inference

33. Other sources
• Last fm: {"lastfm":[
• {"genre":"Classical", "score": 2, "score2": 2.30102999566398 },
• {"genre":"Folk", "score": 6, "score2": 2.77815125038364 },
• {"genre":"Desi", "score": 1, "score2": 2.0 },
• {"genre":"Easy Listening; Soundtracks and Musicals", "score": 9, "score2": 2.95424250943932 },
• {"genre":"Rock and Indie", "score": 190, "score2": 4.27875360095283 },
• {"genre":"Country", "score": 5, "score2": 2.69897000433602 },
• {"genre":"Classic Pop and Rock", "score": 156, "score2": 4.19312459835446 },
• {…
• Facebook: {"education":{"degree":"PhD","institute":"University of
Amsterdam"},"work":{"employer":"NoTube incorp.","sector":"Web and Media","income":"EUR 40000","years":2},"basic
information":{"sex":"Male","hometown":"Amsterdam","home neigborhood":"Zuid-As","relationship":"Open
relationship","political views":"Liberal","religious views":"None"},"personal":{"activities":"snowboarding, music,
karate","interests":"cooking, travel","favorite music":"Fatboy slim, Rammstein, Mahler","favorite movies":"Blues Brothers,
Lord of the Rings","favorite books":"The Hitchhiker's Guide to the Galaxy, Discovery of Heaven, The Island of the Day
Before"}}

34. Inference

35. Recommendation

36. Inference

37. Bottom line
• TV recommendation and Social Web
• Align and enrich to aggregate heterogeneous data
• User data aggregation component: The BeanCounter
• Prototype with iZapper
Coming more soon in this theater

38. Thank you
(CC) NOTUBE MMIX