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An Ontology Based Recommendation System
An Ontology Based Recommendation An Ontology Based Recommendation System for Elderly and Disabled PersonsSystem for Elderly and Disabled Persons
Ingo Zinnikus, Anton Bogdanovich, Ralph Schäfer
An Ontology Based Recommendation System
Structure of the talkStructure of the talk
• SAID: Project Overview– Objectives– General System Description
• Information Service– Agent based personalised access to
the web– Basic features of the recommendation
system: concept representation, probability calculation
An Ontology Based Recommendation System
Introduction: SAID Introduction: SAID Project DataProject Data
• Contract Number: IST-2000-25024• Started: 1st January 2001.• Duration: 30 months.• Participants:
– EPTRON SA. Spain. (Project Coordinator)– VIA DIGITAL. Spain.– Ayuntamiento de Madrid. Spain.– VESTIA Housing. Netherlands.– CASEMA. Netherlands.– City of The Hague. Netherlands.– DFKI. Germany.– University of Edinburgh. UK.
An Ontology Based Recommendation System
Introduction: Objectives. Introduction: Objectives.
Objectives:• Improve the Quality of life of the Disabled & Elderly by
contributing to their independent living.• Integrate the Disabled & Elderly in the IST.• Improve and optimise processes and methods for Service
providers.• Advance the state of the art in the field of Digital TV
Interactive services: interfaces, communications, MHP, etc.• Advance the state of the art in the field of personalised
assistants based on Intelligent Agents.As a consequence:• Develop an Integrated Platform able to provide remote
services for the Disabled & Elderly.• Develop innovative Tools for the Service Providers.
An Ontology Based Recommendation System
Introduction: Innovation. Introduction: Innovation.
• Digital TV: MHP• Active system: Autonomous Agents.• Simplified Interfaces.• Mobile phones.• Complete integrated Service.• Massive Audience.• Viable technologies in an emerging Sector.• Proposed Advantages:
– Reduce Costs.– Extend the number and scope of users.– Extend the Catalogue of Services.– Offer Added Value Services
An Ontology Based Recommendation System
Introduction: EnvironmentsIntroduction: Environments
Which is the Environment of the End-Users?• Domestic Users at Home.• Mobility Restrictions.• Health Problems.• Accessibility Problems.• Isolation.• Loneliness.• No computer skills.Which is the Environment of the Service Providers ?• Personnel shortage.• Continuous increase of Demand.• Long Waiting Lists.• High costs.• 24h attention. Not possible.• Lack of technical resources.• Lack of technical skills.• Only basic demands are covered.
An Ontology Based Recommendation System
Introduction: Project DescriptionIntroduction: Project Description
Which is our Proposal ?• Increase the quality of life by promoting independent living.• Increase processes automation and cost reduction.• Extend the catalogue of services.• Offer educational possibilities.• Increase opportunities for personal communication. • Use already existing technical infrastructure.• Strong emphasis on personalisation.How can we do it ?• Personalised attention through remote services.• The TV set as the only interface to the user: DTV.• User Interfaces specifically designed for the Disabled & Elderly.• Active System: Intelligent Software Agents maintain separate personal user profiles
for each user.• Tools to automate and ease the job of social assitants: medical histories, planning,
user profiles, agenda, WAP.• Central Facility able to provide 24h Attention: Videoconferencing, alarms,
supervision, advisers, etc.
An Ontology Based Recommendation System
User environmentUser environment
• OpenTV technology.• 2nd generation STB.• Oversimplified user
interface.• Modem link with server
subsystem.• Information:
– Active mode.– Interactive mode.
• Reminders:– Five reminder types.
• Alarms:– Confirmation.– Reception & Attention
feedback.• Other:
– Services menus.– Entertainment.
Data line Set-Top Box
Remote control
Digital TV input
TV set
An Ontology Based Recommendation System
Client Platform. Services MenuClient Platform. Services Menu
Options:• Information• Videoconference• Education• Shopping• Household• Back (to TV)• Entertainment
Characteristics:• Clean Design.• Few Concepts.• Two keys• Few Key Strokes.• Few Levels.• “Intelligent .”• Graphics & Sound.
An Ontology Based Recommendation System
SAID Information Service: Two modesSAID Information Service: Two modes
Interactive Mode:• Information explicitly requested by the user.• Simple user interface.• Personalised list of topics.• Search trees automatically pruned by Intelligent Agents based on
user profile.
Active Mode:• Information automatically searched by the intelligent agents
based on user profile.• Use while watching TV.• Non Disturbing: Flashing Icon -> Summary -> Complete
information
An Ontology Based Recommendation System
Sources for Information ServicesSources for Information Services
• World Wide Web (WWW)– Direct access of Web pages– Search Bots
• Databases– Internal: SAID database– External: Service providers
An Ontology Based Recommendation System
Motivation: Why not using a browser?Motivation: Why not using a browser?
• Elderly and disabled people often are reluctant to use the internet because of technophobia.
• The advantage of a browser is a disadvantage for elderly people!
• The user receives not only information s/he looks for but also advertising, “spam” etc.
• Hyperlinks are confusing and lead to information overflow.
An Ontology Based Recommendation System
Solution: Pre-Selected web pagesSolution: Pre-Selected web pages
• Only a limited set of web pages is selected in order to correspond the needs of elderly and disabled people.
• Web pages are analysed and categorised in advance. Each web page we use has an associated node in an “ontology tree”.
• There is no need to spend time at searching a web page for interesting information
An Ontology Based Recommendation System
OntologyOntology
TV
Gardening
Wellness
Movie
Sport
Plants
Tips
Seniors
Health
Diseases
All
Simple Ontology
The ontology in SAID serves not only as a knowledge storage but also has a functional constituent.
Each of the tree nodes serves as a container for additional data that points to a specific web page and helps to extract only desirable information from it using predefined rules
An Ontology Based Recommendation System
Hierarchical StructureHierarchical Structure
Browsing by iterated selection of concepts All
TV Wellness Gardening
MoviesSport Soap Operas
Comedy Action ScienceFiction
Ontology tree
Action
An Ontology Based Recommendation System
Dynamical InformationDynamical Information
Parsing the web page
ActionURL
RULES
Snatch Gladiator
An Ontology Based Recommendation System
Ontology: The “Configurator” tool.Ontology: The “Configurator” tool.
• Consists of a tree of predefined topics
• Additional fields for web sites and rules to extract information which can be changed and modified e.g. by a social worker
• Gives the ability to add/rename/delete the concepts of ontology.
• Allows to keep in touch with rapidly changing internet environment and quickly react to it’s changes without any modification of the program’s code.
An Ontology Based Recommendation System
From an Ontology to User PreferencesFrom an Ontology to User Preferences
• The ontology tree is more a general representation of interesting topics for a type of user than a representation of a specific user’s preferences.
• In order to generate user preferences, we annotate every concept with a supplementary index which represents the user’s interest in this topic.
• Question: how to adapt this index to the user’s preferences?
An Ontology Based Recommendation System
User Preferences are part of user profileUser Preferences are part of user profile
namesurnameid...preferences
...
PreferencesUser Profile
An Ontology Based Recommendation System
Steps towards a recommendationSteps towards a recommendation::
• Adapting to the real preferences according to the decisions of the user– Individual estimates of the user’s interests
– The more interactions, the better the estimates
• Predicting possible user interest for a specific object
(web site, text, image, etc.) on the basis of previous decisions
An Ontology Based Recommendation System
Unobtrusive monitoring of the user’s actionsUnobtrusive monitoring of the user’s actions
• Observation: An object is presented to the user. The user accepts or
rejects the object.
• Meaning: If the user accepts (rejects) the object, her overall evaluation
of the object is probably very high (low).
An Ontology Based Recommendation System
Counting acceptances and occurrences of topicsCounting acceptances and occurrences of topics
Sport
Football
Racing
Basketball
6/30
2/30
12/30
4/30
6/31
2/31
12/31
4/31
6/31
2/31
13/31
5/31
Itemspresented
RacingRacing
An Ontology Based Recommendation System
Bayesian Reasoning for tree-like User PreferencesBayesian Reasoning for tree-like User Preferences
)(
)()|(
BP
BAPBAP
)(
)()|(
SportP
SportRacingPSportRacingP
)(
)()|(
SportP
RacingPSportRacingP
)()( RacingPSportRacingP
385.03113
315)|( SportRacingP
For conditional probabilities we have
This means for the example
Since in the case of our ontology we have
We can therefore conclude
which gives us in our example
An Ontology Based Recommendation System
Shortcoming of this solutionShortcoming of this solution
• Calculation only with expected value, but no probability distribution and variance
• Variance would allow a better estimation of the reliability of a suggestion
use of full Bayesian networks to model more differentiated behaviour !?
An Ontology Based Recommendation System
Bayesian Networks Bayesian Networks
• Probabilistic inference mechanism• Off-the-shelf tools available for reasoning• Technical properties
– Nodes correspond to random variables: uncertainty is represented in form of probability distribution.
– Edges represent uncertain relations, represented as conditional probability tables.
– Standard algorithms to evaluate networks.
An Ontology Based Recommendation System
Combining Hierarchy with Bayesian netsCombining Hierarchy with Bayesian nets
A subtree represents a Bayesian net only if the concepts within this subtree are independent of each other.(E.g. ‚TV‘ is a media and therefore not independent of e.g. ‚Wellness‘)
All
Wellness TV
Movie Sport Politics
Action
Preferences
Bayesian net
[0.0,0.1][0.1,0.2][0.2,0.3][0.3,0.4][0.4,1.0]
[0.0,0.1][0.1,0.2][0.2,0.3][0.3,0.4][0.4,1.0]
(3 times)
An Ontology Based Recommendation System
Crossing link between branches in the hierarchyCrossing link between branches in the hierarchy
Identical or synonymous concepts can be linked together and ...
All
TV Wellness Gardening
EmissionSport Soap Operas
Travelling Gardening Politics
Preferences
An Ontology Based Recommendation System
Concept is incorporated into the Bayesian netConcept is incorporated into the Bayesian net
... are integrated into a larger Bayesian net
Gardening
Emission
Travelling Gardening Politics
[0.0,0.1][0.1,0.2][0.2,0.3][0.3,0.4][0.4,1.0]
(3 times)
[0,1][1,2][2,3][3,4][4,10]
An Ontology Based Recommendation System
The Recommendation functionalityThe Recommendation functionality
• On each level an additional node ‘Recommendation’ is offered to the user
• After choosing ‘Recommendation’ concepts in the current subtree with a probability higher than a specific threshold are presented
Browsing the ontology tree is facilitated
An Ontology Based Recommendation System
ConclusionConclusion
• We presented the SAID system which provides support for elderly and disabled persons
• Information service replaces traditional web Browser by browsing through an ontology tree
• Tree-like ontology allows Bayesian calculation of conditional probabilities as a basis for a recommendation system
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