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The NoTube BeanCounter: Aggregating User Data for Television Programme Recommendation
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The NoTube BeanCounter: Aggregating User Data for Television Programme Recommendation






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    The NoTube BeanCounter: Aggregating User Data for Television Programme Recommendation The NoTube BeanCounter: Aggregating User Data for Television Programme Recommendation Presentation Transcript

    • 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
    • FP7 Integrated Project • Networks and ontologies for the transformation and unification of broadcasting and the Internet • Research • Broadcast & Telecommunication • Industry • Dissemination & Training
    • Objectives
    • Killer Applications • Personalized semantic news • Personalized TV guide with adaptive advertising • Internet TV in the Social Web
    • Programmes lifecycle
    • 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 Hyper ego FredCavazza.net.
    • Television and Social Web
    • Enrichment
    • 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
    • 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?)
    • The BeanCounter
    • Trivial recommendations series I like series my friends like Broadcast and on‐demand availability data Dr. Who is on tomorrow at 23:20
    • Recommendation Explanations
    • ProtoType
    • Inference
    • Inference
    • bbc.co.uk (po) http://www.bbc.co.uk/programmes/b00mqjhr.rdf http://www.bbc.co.uk/bbcone/programmes/schedules/london.xml
    • Inference
    • 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>
    • Aligning Vocabularies • Alignment of Genre vocabularies – manual, small number – xmltv:documentaire  tva:documentary – imdb:thriller  tva:thriller – imdb:sci-fi  tva:science_fiction
    • 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
    • Inference
    • EPG; content + actions
    • Inference
    • iZapper
    • Viewerlog
    • Inference
    • User profile
    • Inference
    • 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"}}
    • Inference
    • Recommendation
    • Inference
    • 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
    • Thank you (CC) NOTUBE MMIX