AVATAR: Modelling Users by Dynamic Ontologies in a TV

Recommender System based on Semantic Reasoning

Alberto Gil SollaDepartment of Telematic Engineering

University of Vigo (Spain)

EuroITV 2005: the 3rd European Conference on Interactive TelevisionAalborg, Denmark

April 1, 2005

Outline of this presentation

• AVATAR: A TV recommender system

• User Modelling based on ontologies

• Updating user profiles

• Conclusions and Further Work

Outline of this presentation

• AVATAR: A TV recommender system

• User Modelling based on ontologies

• Updating user profiles

• Conclusions and Further Work

AVATAR: Motivation

• Migration from analogue to digital TV

• Implications:– More channels in the same bandwidth– Software applications mixed with audiovisual

contents

• Users will need help to find interesting contents (programs and applications) among irrelevant information

Content Recommenders

• Different approaches to recommend personalized TV contents: – Bayesian methods

– Content-based techniques

– Collaborative filtering

• A common drawback related to the reasoning capabilities: no knowledge about the TV domain is involved in the algorithms

AVATAR• AdVAnced Telematic search of Audiovisual

contents by semantic Reasoning

• Framework to test recommendation strategies:

– Profiles matching (collaborative filtering)

– Semantic reasoning about the user preferences and TV programs (enhanced content-based technique)

• Knowledge base in AVATAR: an OWL ontology about the TV domain– Hierarchies of classes and properties– Specific instances extracted from TV-Anytime program

descriptions

BayesianAgent

SemanticAgent

ProfilesAgent

LocalAgent

Contentcapture

Combiner

UsersDatabase

G-REC

Ontology

ProfilesRecommendations

User Actions

Personal data Preferences History

DTVTransport

Stream FeedbackAgent

Private data

MHP TV-Anytime API

B-REC

S-REC

P-REC

Recommenders

MHP Application

SetTop Box

AVATAR architecture

TV-Anytime<ProgramInformation programId="crid://www.uvigo.es/2012032">

<BasicDescription>

<Title type="seriesTitle">Start Trek</Title>

<Synopsis> Long, long time ago, and far, far, far away… </Synopsis>

<Keyword>fiction</Keyword> <Keyword>space</Keyword>

<Genre href="urn:tva:metadata:cs:ContentCS:5.1" type="main"/>

<ParentalGuidance>

<mpeg7:ParentalRating href="urn:mpeg:mpeg7:cs:MPAAParentalRatingCS:G">

<mpeg7:Name>G</mpeg7:Name> </mpeg7:ParentalRating>

<mpeg7:Region>ES</mpeg7:Region>

</ParentalGuidance>

<Language type="original">en</Language>

<CreditsList>

<CreditsItem role="urn:mpeg:mpeg7:cs:MPEG7RoleCS:ACTRESS">

<PersonNameIDRef ref="PN15"/>

</CreditsItem>

</CreditsList>

.......

TV ontology structure

TV Contents

Informative Movies

Incidents News

Economy Political

Action Comedies

TV Ontology

Outline of this presentation

• AVATAR: A TV recommender system

• User Modelling based on ontologies

• Updating user profiles

• Conclusions and Further Work

User Modelling based on Knowledge

• Personal data (static) and preferences about TV programs (dynamic)

• We reuse the TV ontology for user modelling

• User profiles are named ontology-profiles– They are OWL ontologies built incrementally,

as the system receives information about the user viewing behaviour

– They store: classes, their instances, the hierarchical relations, sequences of properties

Ontology-profile TV Contents

Informative Sports

News

Meteorology Political

Football

Formula 1

Live Broadcasts

Historicalreviews

Debate EU Constitution

Niki Lauda biography

Next weekendWeather forecast

San MarinoGrand Prix

Match

LiverpoolAjax

hasTeam Liverpool

hasPlaceAmsterdan

Arena

Textual representation

Sports Football.Match. (hasTeam[Liverpool] p hasPlace[Amsterdan Arena])

c

Formula 1. Live broadcasts. hasPresenter.hasName[Alain Prost]

Movies Comedy_Movies. (hasTitle[The Mask] p hasActor.hasName[J. Carrey])

Outline of this presentation

• AVATAR: A TV recommender system.

• User Modelling based on ontologies

• Updating user profiles

• Conclusions and Further Work

Ontology profiles: Updating process

• AVATAR infers information from the actions carried out by the viewers

• Indexes for updating user profiles referred to each class and each instance

– Degree of Interest (DOI)

– Confidence (Conf)

– Relevance (Rel)

Degree of Interest (DOI)• Level of interest referred to a class/instance for

a user

• Several factors have influence on its calculation:

– Index of Feedback (IOF): Feedback information referred to the suggestions selected or rejected

– Antiquity of Viewing (AOV): The time from the user selects a program until he/she watches it

– Index of Viewing (IOV): Ratio between the viewing time and the content duration

Degree of Interest (II)Old DOI of instance Instk (before updating)

New DOI of instance Instk (after updating)

1

( ) ( )N

ii k

k

DOI C DOI Inst

( ) ( )( ) ( )

( )

i ii i k kk o k i

k

IOV Inst IOF InstDOI Inst DOI Inst

AOV Inst

( )io kDOI Inst

( )ikDOI Inst

The index of a class is computed by addingthe contribution of each instance of that class

Confidence index

• It quantifies the success or failure obtained by AVATAR in previous recommendations

• It is based on the order of the selected or rejected programs

( )( ) ( )

( )

ii i kk o k i

k

IOF InstConf Inst Conf Inst

Order Inst

1

( ) ( )N

ii k

k

Conf C Conf Inst

Relevance index• Combination of DOI and Confidence indexes

• Used to order the programs offered to end users

• Classes with high relevance provide the recommendation with many instances

e ( ) e ( ) ( ) ( )i i i ik o k k kR l Inst R l Inst DOI Inst Conf Inst

1

e ( ) e ( )N

ii k

k

R l C R l Inst

Relevance (C)

User choicesC1

1

C2 C3

Scenario 1

Scenario 2

Scenario 3

-1

Relevance index

Outline of this presentation

• AVATAR: A TV recommender system

• User Modelling based on ontologies

• Updating user profiles

• Conclusions and Further Work

Conclusions

• Ontology-profiles favour inferential processes to improve the offered suggestions

• Indexes flexible enough to maintain the user preferences permanently updated

Further Work • Spread the indices to adjacent classes

• Collaborative filtering process based on semantic reasoning– The goal is to compare different user preferences, by

inferring implicit relations between them

• Approach of user modelling can be easily extended to applications of the Semantic Web (Web services)