future tv is now: personalized & social
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Keynote at TV2PM workshop @UMAP2012 ConferenceTRANSCRIPT
Future TV is Now: Personalized & SocialLora Aroyo
VU University Amsterdam
@laroyoMonday, July 16, 12
Monday, July 16, 12
Monday, July 16, 12
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Monday, July 16, 12
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Observations I
The TV world was closed, but now ...
• TV is the Web
• TV is Social
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Observations II
Personalized access to TV content is demanded, but ...
• data challenges multiply with Web openness• good social recommendations are not trivial • it is not about (single) algorithms any more• it is about metrics
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Observations III
Paradigm shift in personalized applications methodology
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Challenges
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Web vs. TV
TV is the Web
© Ugli. on the Walls of BBC London
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© Dan BrickleyMonday, July 16, 12
TV is SocialMonday, July 16, 12
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42% of UK adults who use Internet while watching TV also discuss or comment on programs they are watching
© Ericsson ComsumerLab: TV & Web Consumer Trends 2011
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Personalization Needed© Kierstin Shaylee
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choices, choices ...
© VickyBuser, BBC
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lost in space© RedBee Slides at MIPCube2012
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always missing something© RedBee Slides at MIPCube2012
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demand for experienceMonday, July 16, 12
Recommendations© Ericsson ComsumerLab: TV & Web Consumer Trends 2011
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Social Recommendations© Ericsson ComsumerLab: TV & Web Consumer Trends 2011
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Social Recommendations
I usually find NEW programmes to watch by:
RESULTS OF NOTUBE SOCIAL WEB & TV SURVEY 2012
© NoTube Studies @ BBC
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Social Recommendations
I usually find NEW programmes to watch by:
RESULTS OF NOTUBE SOCIAL WEB & TV SURVEY 2012
Social TV
RESULTS OF NOTUBE SOCIAL WEB & TV SURVEY 2012
© NoTube Studies @ BBC
Monday, July 16, 12
© RedBee Slides at MIPCube2012
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Social & Personalized TV
© Ugli. on the Walls of BBC London
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ScenariosMonday, July 16, 12
User Perspective
• what is the role of social recommendations?• how do people watch TV together?• how devices influence watching?• what are perceived trade-offs for privacy vs. personalization?
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Technology Perspective
• what is the role of metadata in TV & Apps?• what are ways to share information in real time?• what are ways to sync TV & other metadata?• what is an easy way for devices to find & talk to each other?
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http://www.youtube.com/watch?v=0QUPKQLvOHU
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the old TV model is broken
closed, proprietary, static, impersonal
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• access to basic metadata about programs • links are the basic currency of social media
• URIs for the things you watch • links to related Web entities
• even a small amount of metadata enables interesting apps
Open Web Standards & Data
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TV Program Enrichment
SemanticPattern-based
Recommendation Strategy
RDF GraphTV
Programs
Semantic ContentPatterns for
TV Programs
HybridRecommendation
Strategy
StatisticalSimilarity-based
Recommendation StrategyUser Ratings &
Demographics(BBC EPG
Data)
EPG Metadata(BBC)
Recommendation Service
SimilarityClusters
of Programs
User Data Analysis
End-UsersEnd Users
Linking Web Data to User ProfilesMonday, July 16, 12
TV Preference Data: Sparse
• Even for a single service (e.g. Netflix)
• NoTube’s open systems: challenges multiply
• often no global view, only per-user data
• many ways of identifying same content item
• many ways of identifying same user
• many ways of identifying entities e.g. actors, directors, ...
© Dan BrickleyMonday, July 16, 12
TV Preference Data: Fragmented
© Dan BrickleyMonday, July 16, 12
TV Preferences in NoTube
• Inferred from the Social Web
• tweets, FB likes, last.fm listened, etc.• weighted interests
• Represented using Linked Data web identifiers
• record-linkage:
• NLP:
facebook.com/pages/ dbpedia.org/
http://dbpedia.org/resource/Matt_Lucashttp://dbpedia.org/resource/David_Walliams
#littlebritain
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TV Preferences in NoTube
• Inferred from the Social Web
• tweets, FB likes, last.fm listened, etc.• weighted interests
• Represented using Linked Data web identifiers
• record-linkage:
• NLP:
facebook.com/pages/ dbpedia.org/
Brilliant british humor by Matt Lucas & David Walliams - whole range of facinating characters portraying diversity of british society
http://dbpedia.org/resource/Matt_Lucashttp://dbpedia.org/resource/David_Walliams
#littlebritain
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NoTube’s Beancounteraggregate, analyze & profile
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NoTube’s User Activitiesaggregate
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NoTube’s User Interestsprofile
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NoTube’s User Explanationscontrol
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Recommendations in NoTube
• surface interesting, new & relevant programs to individual and group users• combine in a complementary way different statistical & semantic approaches• define metrics for, e.g. serendipity, diversity, relevance
© Dan Brickley
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Analysis of LOD SourcesDataset LinkedMDB DBpedia Freebase
#triples 6,147,978 385,000,000 337,203,427
#props 221 1643 n.a.
#types 53 3,640,000 12,000,000
Properties #triples
foaf:page 512,944
http://purl.org/dc/terms/date 74,111
foaf:made 53,180
linkedmdb:actor_actorid 50,603
dbprop:hasPhotoCollection 30,354
Dataset Enriched BBC
#triples 240,630
#props 41
#types 17,029
Type Pattern #
People person - co_participation - person 62162
People person - actor - person 24843
Format program - has_format - format 13108
Genre program - has_genre - genre 12767
People person - influencedBy - person 10053
People person - partner - person 10053
Award program - award_won - award 782
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From Predictability to Serendipity
Monday, July 16, 12
From Predictability to Serendipity
Paul Merton looks at
Alfred Hitchcock
SecretGardens
Make ‘em laugh
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From Predictability to Serendipity
Paul Merton looks at
Alfred Hitchcock
SecretGardens
Make ‘em laugh
documentaryformat
format
format
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From Predictability to Serendipity
homes and gardens
genre
Grammy Awards
genre
genre art culture and the media
award
award
Paul Merton looks at
Alfred Hitchcock
SecretGardens
Make ‘em laugh
Basic patternsdocumentaryformat
format
format
Monday, July 16, 12
From Predictability to Serendipity
homes and gardens
genre
Grammy Awards
genre
genre art culture and the media
award
award
PaulMerton
anchor
partner
CarolineQuentin
Life ofRiley
actor
Paul Merton looks at
Alfred Hitchcock
SecretGardens
Make ‘em laugh
Basic patternsHomogeneous patterns
documentaryformat
format
format
comedy
genre
Monday, July 16, 12
From Predictability to Serendipity
homes and gardens
genre
Grammy Awards
genre
genre art culture and the media
award
award
PaulMerton
anchor
partner
CarolineQuentin
Life ofRiley
actor
SpikeMilligan
influence
The adventuresof Barry
McKenzie
actor
film
genr
e form
at
Paul Merton looks at
Alfred Hitchcock
SecretGardens
Make ‘em laugh
Basic patternsHomogeneous patterns
documentaryformat
format
format
comedy
genre
Monday, July 16, 12
From Predictability to Serendipity
homes and gardens
genre
Grammy Awards
genre
genre art culture and the media
award
award
PaulMerton
anchor
partner
CarolineQuentin
Life ofRiley
actor
SpikeMilligan
influence
The adventuresof Barry
McKenzie
actor
film
genr
e form
at
Paul Merton looks at
Alfred Hitchcock
SecretGardens
Make ‘em laugh
CharlieChaplin
synopsisenrichment
ShanghaiKnights
form
at
action andadventure
genre
JackieChan
actor
influence
Basic patternsHomogeneous patternsHeterogeneous patterns
documentaryformat
format
format
comedy
genre
Monday, July 16, 12
From Predictability to Serendipity
homes and gardens
genre
Grammy Awards
genre
genre art culture and the media
award
award
PaulMerton
anchor
partner
CarolineQuentin
Life ofRiley
actor
format
synopsisenrichment
Alfred Hitchcock
director
Suspicion
AcademyAwards
awar
d
Thriller
genre
SpikeMilligan
influence
The adventuresof Barry
McKenzie
actor
film
genr
e form
at
Paul Merton looks at
Alfred Hitchcock
SecretGardens
Make ‘em laugh
CharlieChaplin
synopsisenrichment
ShanghaiKnights
form
at
action andadventure
genre
JackieChan
actor
influence
Basic patternsHomogeneous patternsHeterogeneous patterns
documentaryformat
format
format
comedy
genre
Monday, July 16, 12
Weak Semantics & Strong Statistics
Monday, July 16, 12
Weak Semantics & Strong Statistics
Paul Merton
looks at Alfred Hitchcock
PaulMerton
anchor partnerCarolineQuentin
Life ofRiley
act
ordocumentary
form
at
art culture and the media
genreMonday, July 16, 12
Weak Semantics & Strong Statistics
Paul Merton
looks at Alfred Hitchcock
PaulMerton
anchor partnerCarolineQuentin
Life ofRiley
act
ordocumentary
form
at
art culture and the media
genre
woman25 years old1 child
Userprofile
Monday, July 16, 12
Weak Semantics & Strong Statistics
Paul Merton
looks at Alfred Hitchcock
PaulMerton
anchor partnerCarolineQuentin
Life ofRiley
act
ordocumentary
form
at
art culture and the media
genre
25 year old women with
1 child
woman25 years old1 child
Userprofile
Monday, July 16, 12
Weak Semantics & Strong Statistics
Paul Merton
looks at Alfred Hitchcock
PaulMerton
anchor partnerCarolineQuentin
Life ofRiley
act
ordocumentary
form
at
art culture and the media
genre
25 year old women with
1 child
Similar users
woman25 years old1 child
Userprofile
Monday, July 16, 12
NoTube’s N-Screen
N-Screendrag & drop, sharing & TV control
drag & drop, shuffle, sharing & TV control
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NoTube’s N-Screenexplore program
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NoTube’s N-Screensimilar programs
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• means of surfacing content buried in the video collection• when users might reach a dead-end with the recommendations • adds an extra element of serendipity
NoTube’s N-Screenrandom selection
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NoTube’s N-Screenexplanations
• depending on recommendation strategies• depending on the user’s task
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♥
INTERESTING PROGRAMMES
watching with friends
suggestions based on programmes others have watched
linking programmes by guests, actors, presenters etc.
sharing suggestions with others in the same room
?
watching 'together apart' in different locations
changing programmes on someone else's TV
♥♥
♥
✔
✘
suggestions based on your past 'Social Web' activities
controlling the TV
drag & drop
swapping suggestions with specific friends
sharing them
✔
♥
✔
♥
sharing to watch immediately
http://notube.tv
? explaining recommendations
'shuffle' option for programme selection
?
finding
USER TESTING RESULTS December 2011, BBC R&D London10 participants : 5 male, 5 female, aged between 20 to 64
drag & drop to change what's playing on TV
♥ ✔ ✘ ? loved liked disliked unsure
For more details:
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More from NoTube ...
http://notube.tv http://www.slideshare.net/NoTubeProject
@notubeproject
© Dan Brickley
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Challenges
• data challenges multiply with Web openness
• good social recommendations are preferred
• it is not about (single) algorithms any more
• it is about the metricsMonday, July 16, 12
What’s next
• The move to IP - opportunity to discover what people watch in greater breadth and depth• gather consumers’ viewing behavior & video streams• combine them with enhanced EPG as input for a holistic live-stream data mining analysis
• generate a high-quality linked open dataset (LOD) describing live TV programming• market research on viewing behavior
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Thank you
• for their slides & visuals:• Dan Brickley, Valentina Maccatrozzo, • Vicky Buser, Libby Miller, • Davide Palmisano, and the whole NoTube team
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Monday, July 16, 12