The document discusses the future of personalized and social TV through a project called NoTube. NoTube aims to integrate TV and the web using semantics by openly linking TV content and user data. It seeks to put users back in control by connecting their personal data from different sources with explicit semantics. The project also aims to make TV accessible across devices and allow mobile devices to function as remote controls.
Why didn’t we foresee the rise of social TV?
Social TV is the biggest change in television since it was invented.
Audiences are increasingly engaging with television via second screens (laptops, mobiles and tablets) and connected TV systems. This transforms medium and industry and gives social networks key commercial roles in the TV business.
The rise of social TV raises a crucial issue for our understanding of forecasting and innovation:
Why did we not foresee this major development in television?
The Futurescape presentation Social TV, Forecasting and Innovation reveals how 1995 predictions about the future of TV missed social TV and proposes how such blind spots in forecasting can be remedied.
The presentation covers
Social TV: a synthesis of TV and social networking
1. Transforming the medium of TV
2. A radical shift in power for the TV industry
3. How does social TV power manifest itself?
Forecasting and Innovation
4. The future of TV as seen from 1995
5. What we didn’t foresee in 1995 – social TV
6. Why didn’t we anticipate it?
7. Implications for forecasting and innovation
For more insights into the future of social media and television, download our white paper How Connected Television Transforms The Business of TV (adapted from Futurescape’s strategy report, Social TV).
Social TV: How Facebook, Twitter and connected television transform global TV...Futurescape
Facebook and Twitter are fighting for key roles in the worldwide television market, particularly TV advertising and pay-TV, as Internet-connected television makes TV into a social medium.
This report is the first critical appraisal of how the battle between the two major social networks over social TV is shaping television and challenging the TV industry.
Verovert TV het internet, of andersom?, Presentation for the Dutch Crossmedia MBA, about TV and Internet by Jeroen Verkroost of http://www.copypaste.co.uk
An introduction to HbbTV Hybrid Broadcast Broadband TV. What is it? How does it work? Red button and #HbbTV Service examples.
Presented at Thailand's Engineering Expo November 29, 2014 and at Thailand's Set-top box Committee's Public Hearing at Thailand's Engineering Institute (EIT) November 20, 2014.
Why didn’t we foresee the rise of social TV?
Social TV is the biggest change in television since it was invented.
Audiences are increasingly engaging with television via second screens (laptops, mobiles and tablets) and connected TV systems. This transforms medium and industry and gives social networks key commercial roles in the TV business.
The rise of social TV raises a crucial issue for our understanding of forecasting and innovation:
Why did we not foresee this major development in television?
The Futurescape presentation Social TV, Forecasting and Innovation reveals how 1995 predictions about the future of TV missed social TV and proposes how such blind spots in forecasting can be remedied.
The presentation covers
Social TV: a synthesis of TV and social networking
1. Transforming the medium of TV
2. A radical shift in power for the TV industry
3. How does social TV power manifest itself?
Forecasting and Innovation
4. The future of TV as seen from 1995
5. What we didn’t foresee in 1995 – social TV
6. Why didn’t we anticipate it?
7. Implications for forecasting and innovation
For more insights into the future of social media and television, download our white paper How Connected Television Transforms The Business of TV (adapted from Futurescape’s strategy report, Social TV).
Social TV: How Facebook, Twitter and connected television transform global TV...Futurescape
Facebook and Twitter are fighting for key roles in the worldwide television market, particularly TV advertising and pay-TV, as Internet-connected television makes TV into a social medium.
This report is the first critical appraisal of how the battle between the two major social networks over social TV is shaping television and challenging the TV industry.
Verovert TV het internet, of andersom?, Presentation for the Dutch Crossmedia MBA, about TV and Internet by Jeroen Verkroost of http://www.copypaste.co.uk
An introduction to HbbTV Hybrid Broadcast Broadband TV. What is it? How does it work? Red button and #HbbTV Service examples.
Presented at Thailand's Engineering Expo November 29, 2014 and at Thailand's Set-top box Committee's Public Hearing at Thailand's Engineering Institute (EIT) November 20, 2014.
Similar to NoTube Project Presentation @ IBC2010 (20)
The Rijksmuseum Collection as Linked DataLora Aroyo
Presentation at ISWC2018: http://iswc2018.semanticweb.org/sessions/the-rijksmuseum-collection-as-linked-data/ of our paper published originally in the Semantic Web Journal: http://www.semantic-web-journal.net/content/rijksmuseum-collection-linked-data-2
Many museums are currently providing online access to their collections. The state of the art research in the last decade shows that it is beneficial for institutions to provide their datasets as Linked Data in order to achieve easy cross-referencing, interlinking and integration. In this paper, we present the Rijksmuseum linked dataset (accessible at http://datahub.io/dataset/rijksmuseum), along with collection and vocabulary statistics, as well as lessons learned from the process of converting the collection to Linked Data. The version of March 2016 contains over 350,000 objects, including detailed descriptions and high-quality images released under a public domain license.
FAIRview: Responsible Video Summarization @NYCML'18Lora Aroyo
Presentation at the NYC Media Lab (NYCML2018). There is a growing demand for news videos online, with more consumers preferring to watch the news than read or listen to it. On the publisher side, there is a growing effort to use video summarization technology in order to create easy-to-consume previews (trailers) for different types of broadcast programs. How can we measure the quality of video summaries and their potential to misinform? This workshop will inform participants about automatic video summarization algorithms and how to produce more “representative” video summaries. The research presented is from the FAIRview project and is supported by the Digital News Innovation Fund (DNI Fund), which is part of the Google News Initiative.
DH Benelux 2017 Panel: A Pragmatic Approach to Understanding and Utilising Ev...Lora Aroyo
Lora Aroyo, Chiel van den Akker, Marnix van Berchum, Lodewijk
Petram, Gerard Kuys, Tommaso Caselli, Jacco van Ossenbruggen, Victor de Boer, Sabrina Sauer, Berber Hagedoorn
Crowdsourcing ambiguity aware ground truth - collective intelligence 2017Lora Aroyo
The process of gathering ground truth data through human annotation is a major bottleneck in the use of information extraction methods. Crowdsourcing-based approaches are gaining popularity in the attempt to solve the issues related to the volume of data and lack of annotators. Typically these practices use inter-annotator agreement as a measure of quality. However, this assumption often creates issues in practice. Previous experiments we performed found that inter-annotator disagreement is usually never captured, either because the number of annotators is too small to capture the full diversity of opinion, or because the crowd data is aggregated with metrics that enforce consensus, such as majority vote. These practices create artificial data that is neither general nor reflects the ambiguity inherent in the data.
To address these issues, we proposed the method for crowdsourcing ground truth by harnessing inter-annotator disagreement. We present an alternative approach for crowdsourcing ground truth data that, instead of enforcing an agreement between annotators, captures the ambiguity inherent in semantic annotation through the use of disagreement-aware metrics for aggregating crowdsourcing responses. Based on this principle, we have implemented the CrowdTruth framework for machine-human computation, that first introduced the disagreement-aware metrics and built a pipeline to process crowdsourcing data with these metrics.
In this paper, we apply the CrowdTruth methodology to collect data over a set of diverse tasks: medical relation extraction, Twitter event identification, news event extraction and sound interpretation. We prove that capturing disagreement is essential for acquiring a high-quality ground truth. We achieve this by comparing the quality of the data aggregated with CrowdTruth metrics with a majority vote, a method which enforces consensus among annotators. By applying our analysis over a set of diverse tasks we show that, even though ambiguity manifests differently depending on the task, our theory of inter-annotator disagreement as a property of ambiguity is generalizable.
My ESWC 2017 keynote: Disrupting the Semantic Comfort ZoneLora Aroyo
Ambiguity in interpreting signs is not a new idea, yet the vast majority of research in machine interpretation of signals such as speech, language, images, video, audio, etc., tend to ignore ambiguity. This is evidenced by the fact that metrics for quality of machine understanding rely on a ground truth, in which each instance (a sentence, a photo, a sound clip, etc) is assigned a discrete label, or set of labels, and the machine’s prediction for that instance is compared to the label to determine if it is correct. This determination yields the familiar precision, recall, accuracy, and f-measure metrics, but clearly presupposes that this determination can be made. CrowdTruth is a form of collective intelligence based on a vector representation that accommodates diverse interpretation perspectives and encourages human annotators to disagree with each other, in order to expose latent elements such as ambiguity and worker quality. In other words, CrowdTruth assumes that when annotators disagree on how to label an example, it is because the example is ambiguous, the worker isn’t doing the right thing, or the task itself is not clear. In previous work on CrowdTruth, the focus was on how the disagreement signals from low quality workers and from unclear tasks can be isolated. Recently, we observed that disagreement can also signal ambiguity. The basic hypothesis is that, if workers disagree on the correct label for an example, then it will be more difficult for a machine to classify that example. The elaborate data analysis to determine if the source of the disagreement is ambiguity supports our intuition that low clarity signals ambiguity, while high clarity sentences quite obviously express one or more of the target relations. In this talk I will share the experiences and lessons learned on the path to understanding diversity in human interpretation and the ways to capture it as ground truth to enable machines to deal with such diversity.
Data Science with Human in the Loop @Faculty of Science #Leiden UniversityLora Aroyo
Software systems are becoming ever more intelligent and more useful, but the way we interact with these machines too often reveals that they don’t actually understand people. Knowledge Representation and Semantic Web focus on the scientific challenges involved in providing human knowledge in machine-readable form. However, we observe that various types of human knowledge cannot yet be captured by machines, especially when dealing with wide ranges of real-world tasks and contexts. The key scientific challenge is to provide an approach to capturing human knowledge in a way that is scalable and adequate to real-world needs. Human Computation has begun to scientifically study how human intelligence at scale can be used to methodologically improve machine-based knowledge and data management. My research is focusing on understanding human computation for improving how machine-based systems can acquire, capture and harness human knowledge and thus become even more intelligent. In this talk I will show how the CrowdTruth framework (http://crowdtruth.org) facilitates data collection, processing and analytics of human computation knowledge.
Some project links:
- http://controcurator.org/
- http://crowdtruth.org/
- http://diveproject.beeldengeluid.nl/
- http://vu-amsterdam-web-media-group.github.io/linkflows/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Free Complete Python - A step towards Data Science
NoTube Project Presentation @ IBC2010
1. The
Future
of
TV:
Personalisa3on
&
Social
Seman3cs
Lora
Aroyo
VU
University
Amsterdam
h>p://www.cs.vu.nl/~laroyo/
1
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
2. NOTUBE
PARTNERS
2
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
3. WE
AIM
AT
…
• Integra7ng
TV
&
Web
with
help
of
seman7cs
– Open
and
interlink
TV
content
in
a
Web
fashion
with
Linked
Open
Data
• PuTng
the
user
back
in
the
driving
seat
– Connect
mul7tude
of
distributed
personal
data
with
explicit
seman7cs
• TV
is
not
bound
to
the
device
– Computer
as
TV
&
vice
versa
– Mobile
device
as
remote
control
3
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
4. RESULTS
FOR
…
• VIEWERS
-‐
people
watching
TV
(individuals,
communi7es
and
groups)
• TECHNICIANS
-‐
programmers
(APIs
and
so[ware
components),
broadcasters
and
adver7sers
• SCIENTISTS
-‐
interested
in
standards,
access
policies,
privacy
issues
etc.
4
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
7. PART
OF
THE
OVERALL
TREND
FOR
WEB
&
TV
CONVERGENCE
7
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
8. DRIVEN
BY
INDUSTRIAL
USE
CASES
8
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
9. CURRENT
TRENDS
IN
TV
WATCHING
9
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
10. 10
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
11. From „Social TV: watch Hulu with your Facebook and
MySpace friends“, on Mashable.com
11
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
12. DELOITTE
REPORT:
TV
&
SOCIAL
MEDIA:
FRIENDS
FOREVER?
42%
of
UK
adults
who
use
Internet
while
watching
TV
also
discuss
or
comment
on
programmes
they
are
watching
12
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
13. MULTITUDE
OF
SOCIAL
WEB
&
WEB
TV
ACTIVITIES
13
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
14. 14
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
15. DECIDING
WHAT
TO
WATCH
IS
DIFFICULT
15
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
16. NOT
A
SOLUTION
EITHER
16
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
17. TV
“WE ARE BUILDING A WEB WHERE THE
DEFAULT IS SOCIAL.”
17
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
18. SEMANTICS
FOR
TV
&
USERS
18
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
19. 19
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
20. SEMANTICS
&
LINKED
DATA
IN
TV
• DBPedia,
Freebase,
WordNet(s),
TV
genre
typologies,
IMDB,
TV
Any7me,
BBC
Programme
ontology,
(constantly
growing
list)
• Expose
TV
metadata
as
Seman7c
Web
data
• Use
LOD
concepts
for
EPG
metadata
enrichment
• Publish
NoTube
addi7ons
as
extension
to
LOD
• Combine
and
align
Web
&
TV
standards
(public
broadcasters)
20
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
21. ENRICHMENT
OF
EPG
METADATA
21
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
22. EXAMPLES
OF
SEMANTIC
&
LINKED
DATA
@
BBC
• BBC
Programs
and
BBC
Music
ensure
ONE
page
per
programme
(ar7st)
with
RDF
representa7on
• BBC
Program
Ontology
• BBC
Wildlife
Finder
provides
a
URI
for
every
species,
habitat
and
adap7on
• The
BBC’s
World
Cup
site
uses
RDF
and
Linked
Data
for
a
site
of
700
aggrega7on
pages
22
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
23. USER
DATA
&
ACTIVITIES
Image credits: Dan Brickley
23
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
24. USERS
IN
DISTRIBUTED
CONTEXTS
24
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
25. SOCIAL
WEB
AS
(ISOLATED)
USER
DATA
SILOS
25
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
32. GENERATING
EXPLANATIONS
• Help
users
to:
– Learn
the
recommenda7on
mechanisms
– Understand
why
something
is
recommended
– Quicker
share
recommended
content
– Give
be=er
feedback
to
the
recommender
engine
32
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
33. IDEAS
FOR
RECOMMENDATION
TRENDS
• Twi=er
TV
trends
amongst
my
friends
• What
my
friends
are
watching
• What's
most
popular
on
Twi=er
right
now
• What
my
friends/celebri7es
are
liking
on
FB
• Hunch.com
links
between
content
and
people
stereotypes
• ….
33
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
34. DELIVERING
PERSONALIZED
EXPERIENCE
• Push
personaliza7on
– Filter
automa7cally
irrelevant
content
– Push
relevant
background
content
– Browse
recommenda7ons
• Reduce
the
burden
of
too
much
choice
• Support
(interes7ng)
content
discovery,
serendipity,
knowledge
buiding
• Both
on
TV
and
on
the
Web
34
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
35. NOTUBE
BUILDING
BLOCKS
35
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
36. 36
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
37. DATA
&
METADATA
SERVICES
1. TV
Metadata
(real-‐7me)
Services
170+
channels,
TV
Any7me
metadata
format
2. Metadata
Enrichment
Services
Add
links
to
external
Web
vocab.
&
repositories
3. TV
Vocabulary
&
LOD
Services
Access
to
&
alignment
between
major
TV-‐related
vocabularies,
e.g.
genre
typologies
37
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
38. PERSONALIZATION
SERVICES
1. User
Data
Services
-‐
Access,
aggrega7on
and
standard
representa7on
of
User
Ac7vity
Streams,
e.g.
OpenSocial;
Twi=er,
Facebook
-‐
Trusted
access
to
“friend”
info,
e.g.
Oauth2.0
-‐
User
Profiling
services
2. Recommenda7on
Services
Collabora7ve,
content-‐based
and
hybrid
38
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
40. NOTUBE
DEMONSTRATOR
I:
PERSONALIZED
SEMANTIC
NEWS
40
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
41. NOTUBE
DEMONSTRATOR
II:
PERSONALIZED
EPG
&
ADS
OnlineTV
Guide
Se8op
Box
EPG
Mobile
IdenAty
•
SynchronizaAon
with
STB
• My
TV
Night
• ID
Anywhere
•
SemanAc
Search
• What’s
on
for
me
• NoAficaAons
• Related
Programs
http://ifanzy.nl
41
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
42. NOTUBE
DEMONSTRATOR
III:
SOCIAL
TV
&
WEB
• http://vimeo.com/10553773
• http://vimeo.com/11232681
42
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
43. IMAGE
CREDITS
• h=p://pidgintech.com
• Dan
Brickley,
VU
Amsterdam
• Libby
Miller,
BBC
• Vicky
Buser,
BBC
• Stoneroos
43
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
44. NOTUBE
PARTNERS
44
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
45. The
Future
of
TV:
Personalisa3on
&
Social
Seman3cs
45
Future
of
TV:
Personaliza7on
&
Social
Seman7cs
h=p://notube.tv
Lora
Aroyo
Editor's Notes
How do we bring them together: by enriching EPG Metadata with Linked Data by aggregating Social Web User Activities by semantic recommendations of TV content
TV (closed content) world: proprietary standards, restricted access, paid content Web (open content) world: open standards, universal access, free content
Delicious-> social bookmarking Last.fm->music Identi.ca-> microblogging FOAF Social Graph QDOS-> profile on the web Oauth: securi API
Attention Profile Markup Language Viewer DashBoard Many ways to capture this