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


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


lost in space© RedBee Slides at MIPCube2012


demand for experienceThursday, February 28, 13

Social Recommendations© Ericsson ComsumerLab: TV & Web Consumer Trends 2011


http://notube.tv http://www.slideshare.net/NoTubeProject

@notubeproject


http://notube.tv

http://notube.tv

http://www.slideshare.net/NoTubeProject

http://www.slideshare.net/NoTubeProject

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?


NoTube’s Beancounteraggregate, analyze & profile


NoTube’s User Interestsprofile


NoTube’s N-Screen

N-Screendrag & drop, sharing & TV control

drag & drop, shuffle, sharing & TV control


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?


• 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


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


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


http://dbpedia.org/resource/Matt_Lucas


http://dbpedia.org/resource/David_Walliams


https://twitter.com/#!/search?q=%23littlebritain


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








From Predictability to Serendipity



Paul Merton looks at

Alfred Hitchcock

SecretGardens

Make ‘em laugh




Alfred Hitchcock

SecretGardens

Make ‘em laugh

documentaryformat

format

format



homes and gardens

genre

Grammy Awards

genre

genre art culture and the media

award

award


Alfred Hitchcock

SecretGardens

Make ‘em laugh

Basic patternsdocumentaryformat

format

format



homes and gardens

genre

Grammy Awards

genre


award

award

PaulMerton

anchor

partner

CarolineQuentin

Life ofRiley

actor


Alfred Hitchcock

SecretGardens

Make ‘em laugh

Basic patternsHomogeneous patterns

documentaryformat

format

format

comedy

genre



homes and gardens

genre

Grammy Awards

genre


award

award

PaulMerton

anchor

partner

CarolineQuentin

Life ofRiley

actor

SpikeMilligan

influence

The adventuresof Barry

McKenzie

actor

film

genr

e form

at


Alfred Hitchcock

SecretGardens

Make ‘em laugh

Basic patternsHomogeneous patterns

documentaryformat

format

format

comedy

genre



homes and gardens

genre

Grammy Awards

genre


award

award

PaulMerton

anchor

partner

CarolineQuentin

Life ofRiley

actor

SpikeMilligan

influence


McKenzie

actor

film

genr

e form

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



homes and gardens

genre

Grammy Awards

genre


award

award

PaulMerton

anchor

partner

CarolineQuentin

Life ofRiley

actor

format

synopsisenrichment

Alfred Hitchcock

director

Suspicion

AcademyAwards

awar

d

Thriller

genre

SpikeMilligan

influence


McKenzie

actor

film

genr

e form

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


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


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


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


future tv: connecting web & tv

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