Convergence & Innovation. How software is shaping the future of TV.
Future TV: connecting web & TV
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Transcript of Future TV: connecting web & TV
Semantics for Integrating Web & TV DataLora Aroyo
VU University Amsterdam@laroyo
Thursday, February 28, 13
© Dan Brickley
TV is the Web
Thursday, February 28, 13
Thursday, February 28, 13
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 2011Thursday, February 28, 13
but ...
© VickyBuser, BBC
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lost in space© RedBee Slides at MIPCube2012
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demand for experienceThursday, February 28, 13
© RedBee Slides at MIPCube2012
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Social Recommendations© Ericsson ComsumerLab: TV & Web Consumer Trends 2011
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http://notube.tv http://www.slideshare.net/NoTubeProject
@notubeproject
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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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NoTube’s Beancounteraggregate, analyze & profile
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NoTube’s User Interestsprofile
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NoTube’s N-Screen
N-Screendrag & drop, sharing & TV control
drag & drop, shuffle, sharing & TV control
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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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• 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 Recommendations
• surface interesting, new & relevant programs to individual and group users• combine in a complementary way different statistical & semantic approaches• define NEW metrics for, e.g. serendipity, diversity, relevance
© Dan Brickley
Thursday, February 28, 13
TV Preference Data: Sparse & Fragmented
Even for a single service (e.g. Netflix) 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 BrickleyThursday, February 28, 13
Linking TV Preferences
• 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
Thursday, February 28, 13
Linking TV Preferences
• 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
Thursday, February 28, 13
From Predictability to Serendipity
Thursday, February 28, 13
From Predictability to Serendipity
Paul Merton looks at
Alfred Hitchcock
SecretGardens
Make ‘em laugh
Thursday, February 28, 13
From Predictability to Serendipity
Paul Merton looks at
Alfred Hitchcock
SecretGardens
Make ‘em laugh
documentaryformat
format
format
Thursday, February 28, 13
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
Thursday, February 28, 13
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
Thursday, February 28, 13
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
Thursday, February 28, 13
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
Thursday, February 28, 13
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
Thursday, February 28, 13
Real-Time IPTV Audience Behavior Analysis and Recommendations
opportunity to discover what people watch in greater breadth and depth by gathering consumers’ viewing behavior & video streams and combining them with LOD-enhanced EPG as input for a holistic live-stream data mining analysis
• produce real-time audience research analytics via a stream-analytics process • generate a high-quality TV programing LOD• build a real-time viewing recommendation service that exploits both usage
information and personalized feature extraction
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Prototype Applications
• Broadcaster Dashboard: a dashboard application for broadcasters showing real-time audience statistics and associated social media activity;
• Heartbeat EPG: a program guide enabling viewers to see which programs are currently attracting the greatest attention;
• Infinite Trailers: a continuous sequence of clips of video-on-demand programs equipped with basic remote control
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http://notube.tv
• data challenges multiply with Web openness
• serendipitous (social) recommendations
• real-time recommendations
• it is not about (single) algorithms any more
• it is about the (new) metrics
Challenges
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