NoTube: Stoneroos show case

WP7b

Personalized TV Guide with Adaptive

Advertising

Pieter Bellekens (Stoneroos), Myoung-Wan Koo (KT)

Stoneroos, Thomson Video Networks, Engin, KT

Outline

• WP7b Goals

• NoTube architecture

• Personal Recommendations

• Multi-device PPG application

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Stoneroos Interactive Television www.stoneroos.com

• Multi-device PPG application

• Multi-lingual applications

• DEMO: Personal advertisement

• Evaluation

• Concluding remarks

NoTube 3rd review26-27 March 2012

WP7b Goals

1. Design and development of a Personalized Program Guide (PPG)

2. Recommendation system for TV programs

3. Multi-modal and multi-device aspects of a PPG

4. Multi-lingual PPG environment

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4. Multi-lingual PPG environment

5. Personalized product placement in video streams

26-27 March 2012 NoTube 3rd review

NoTube

Architecture

Multi-platform,

Multi-Lingual and

Multi-modal interfacing

Recommendations as

WP 7b

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Recommendations as

essential part of

Personalization

Keep track of the user

Keep track of all sorts

of heterogeneous data


Frontend

NoTube

Architecture

• As an application within the

NoTube Architecture

• Integration with the

NoTube User Portal via API

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Design and development of a

Personalized

Program Guide

Goal1+2: Personal Recommendations

Recommendation system

for TV programs

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WP7b Recommendations

Weighted content-based recommendation algorithm combined with

collaborative filtering and kickstarted by demographic filtering

• Properties of content are compared to the user profile where we

maintain the user’s feedback with respect to these properties

• Different properties have different weights reflecting their importance

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• Different properties have different weights reflecting their importance

• Feedback is obtained both implicitly and explicitly, and in a context-

sensitive manner

• Demographic filtering used to tackle cold start problem

• Explanations of recommendation to enlighten users in the algorithm’s

working

WP7b Recommendations

Demographics used for

demographic filtering

User Profile

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Likings (optionally contextualized)

used for content-based and

collaborative filtering

Goal 3: Ubiquitous PPG

Yahoo TV Widget

Multi-modal and multi-device aspects of a

Personalized Program Guide

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iPhone app KT’s iPad app


Goal 4: Multi-lingual interfaces

German

Turkish

Korean

Multi-lingual Program

Guide environment

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German


Multimodal/Multilingual

Interface in KT



Interface in KT

• Search Server

– Search and show IPTV contents on mobile devices

( Smart Pad, Smart Phone)

• 8000+ VODs, Live Channels• 8000+ VODs, Live Channels

– Semantic Search by genre, topic, etc. as well as by

movie title.

• 8000 in Korean, 500 genre & topics


Interface in KT

• Multilingual Interface

– Multilingual search by WordNet-based translation

• Concept by WordNet,

• Synopsis & title in English from KMDB• Synopsis & title in English from KMDB

• 1000 in English , 80 genre & topic

• Multimodal Interface for English

– To Recognize by AT&T Speech Mashup Server

• Include Only 80 words by performance problem


Interface in KTKorean Title English Title

살맛납니다 Enjoy Life

이대로, 죽을순없다 Short Time

겨울연가 Winter Sonata

반칙왕 The Foul King

WN 영어 소재 Singer 가수 Fall 가을 Housekeeper 가정부 Domestic Violence 가정폭력 Nurse 간호사 Prison 감옥 Feeling 감정 아홉살인생 When I turned nice

허밍 Humming

Dog 개 Ant 개미 Gang 갱단 tortoise 거북 걸프전 prosecutor 검사 Geisha 게이샤 Game 게임 Winter 겨울 격투기 Marriage 결혼 Economic 경제 Policeman 경찰관 Bodyguard 경호원

Goal 5: Personalized ad placement [DEMO]

Personalized product placement in video

streams

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Integration

with second screen to enhance experience


Evaluation

• Evaluation of the “Why is this recommended” feature

o GOAL: To find out whether providing feedback to the users is actually

useful, appreciated, improving user satisfaction, etc.

o In cooperation with TU/e

o From 312 recruited users, 97 participated throughout the entire study,

which lasted a full week

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which lasted a full week

o At the end a questionnaire was filled-in

o Users were divided in 4 slightly different application incarnations:

* The baseline where only demographic filtering was used

11*

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3

4

CB Recomm off CB Recomm on

Explanation off

Explanation on


Evaluation

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Evaluation results

• Trust:

o User’s trust in the system slightly decreased when explanations are shown

o However, trust improved when people use the system more frequently

• Perceived System Quality:

o Users with higher activity generally perceived the system of higher quality

• Choice Satisfaction:

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• Choice Satisfaction:

o Results show an increase in choice satisfaction with explanations shown

o Again this result was much stronger for people active in the system

• Understandability:

o Results indicate that people understand the system more when they use

the system more frequently, and especially when explanations are shown

• Evaluation of the ad insertion technology from Thomson shown in WP4


Concluding remarks

• Life after NoTube

• Active during NoTube

• NoTube impact factor

• Linked in NoTube

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• Linked in NoTube

• Learned during NoTube

• In retrospect: novel features


Life after NoTube

• Knowledge acquired within the NoTube project is currently exploited and

extended in present (and future) projects:

• Extending the recommendation algorithm to catch and filter even more

user-generated events, and are tailoring the technology for next generation

devices

• Enriching content by including social media information

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• Enriching content by including social media information

as well as visual data including short movies and images

• Investigating the HbbTV standard (which is gaining

popularity) to see how we can further improve the TV

experience by means of personalization and Web content

• Launch of EPGdata, a TV metadata delivery platform

available at http://www.epgdata.tv/

• Main working partners include the big telecom providers among

which we are working with Tele2, KPN, Espial, Vodafone, etc.


Life after NoTube cont.

• International:

• Scanning the Turkish market, together with Engin, to search for business

and market opportunities

• Launching Turkish metadata

within the developed EPGdata

metadata framework

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metadata framework

• Investigation towards other

potential TV metadata buyers

across the world


Active during NoTube 2011

• Participated in the IBC2011 booth

• Where we presented the VOD demo

together with Thomson Video Networks

and KT

• Joined advertising of the NoTube concept

and architecture

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and architecture

• Submitted and presented a publication at the

EuroITV 2011 conference

• “iTV in the Industry” track

• Poster submission titled: “Inclusion of

multiple devices, languages and

advertisements in iFanzy, a personalized

EPG”

• Creation of the Ad insertion demo video


NoTube Impact Factor

• Impact on standards and tech communities:

• Showcase proving the use of egtaMETA (A set of semantically defined

attributes considered to sufficiently describe advertising material)

[http://tech.ebu.ch/egtameta]

• Further prove that the use of Semantic Web technologies (like RDF,

OWL, etc.) is beneficial in domains with complicated data structures and

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OWL, etc.) is beneficial in domains with complicated data structures and

hard to come by metadata

• Further support to the TV-Anytime specification,

describing audiovisual content

• Extra showcases and requirement specifications

as input to the HbbTV consortium (e.g. apps,

profiling, recommendations, data enrichment, etc.)

• Proving the use and benefits of the social TV concept

to a broader audience


Linked in NoTube

• Integration with other WP’s

• Provisioning of and mappings between metadata specifications (IRT)

• Technology and services to place audiovisual material (in this case ads)

within a video streams (Thomson Video Networks)

• Utilisation of the Semantic Broker for enriched TV data (OU)

• Integration of the VOD portal in the NoTube user portal (Polymedia)

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• Integration of the VOD portal in the NoTube user portal (Polymedia)

• Integration with specific partners

• Turkish metadata provisioning (Engin)

• Ad inclusion technology (Thomson Video Networks)

• Multi-model iPad app integrated with the VOD portal (KT)


Learned during NoTube

• That besides the proven advantages of the linked open data approach, also

some challenges emerge with respect to the size of the data sets and how to

retrieve the necessary information in a quick and responsive way

• There is a clear difference between requirements set by a customer who

knows what he wants and requirements at the start of a research project. For

the latter, during the course of a project technology continues to evolve which

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the latter, during the course of a project technology continues to evolve which

can lead to new ideas/insights (e.g. emerge of the tablet)

• During NoTube we gained a great deal of knowledge concerning

apps development and business models on both mobiles, tablets

and connected TVs

• The fact that personalization and providing recommendations

on a TV platform is still a challenging and mind-breaking issue

In retrospect: novel features

• Creation of a TV recommendation system running on top of open

heterogeneous information through Semantic Web technologies

• Enrolment of a PPG system on several platforms (including mobiles,

connected TV, tablets, etc.), all closely integrated and contributing to a

richer user profile

• Integration of next-generation user-computer interaction through

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• Integration of next-generation user-computer interaction through

multi-modal technology

• Automatic and personalized ad placement in video streams potentially

providing next generation business models.

• New insights into future metadata modeling for audiovisual material as

well as advertisements

• Insights have led to a EU patent on a personalized EPG interface


Questions?