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Alberto Gil Solla Department of Telematic Engineering University of Vigo (Spain)

AVATAR: Modelling Users by Dynamic Ontologies in a TV Recommender System based on Semantic Reasoning. Alberto Gil Solla Department of Telematic Engineering University of Vigo (Spain) EuroITV 2005: the 3rd European Conference on Interactive Television Aalborg, Denmark April 1, 2005.

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Alberto Gil Solla Department of Telematic Engineering University of Vigo (Spain)

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  1. AVATAR: Modelling Users by Dynamic Ontologies in a TV Recommender System based on Semantic Reasoning Alberto Gil Solla Department of Telematic Engineering University of Vigo (Spain) EuroITV 2005: the 3rd European Conference on Interactive Television Aalborg, Denmark April 1, 2005

  2. Outline of this presentation • AVATAR: A TV recommender system • User Modelling based on ontologies • Updating user profiles • Conclusions and Further Work

  3. Outline of this presentation • AVATAR: A TV recommender system • User Modelling based on ontologies • Updating user profiles • Conclusions and Further Work

  4. 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

  5. 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

  6. 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

  7. AVATAR architecture SetTop Box MHP Application Recommenders Bayesian Agent B-REC Combiner Profiles Recommendations G-REC Semantic Agent S-REC Ontology Profiles Agent P-REC DTV Transport Stream Content capture Feedback Agent Personal data Preferences History Private data MHP TV-Anytime API Users Database User Actions Local Agent

  8. 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> .......

  9. TV ontology structure TV Contents Informative Movies Action Comedies Incidents News Economy Political

  10. TV Ontology

  11. Outline of this presentation • AVATAR: A TV recommender system • User Modelling based on ontologies • Updating user profiles • Conclusions and Further Work

  12. 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

  13. TV Contents Ontology-profile Informative Sports Football News hasTeam Liverpool Match Amsterdan Arena hasPlace Formula 1 Liverpool Ajax Meteorology Political Historical reviews Live Broadcasts Next weekend Weather forecast Niki Lauda biography Debate EU Constitution San Marino Grand Prix

  14. Textual representation Sports Football.Match. (hasTeam[Liverpool]phasPlace[Amsterdan Arena]) c Formula 1. Live broadcasts. hasPresenter.hasName[Alain Prost] Movies  Comedy_Movies. (hasTitle[The Mask]phasActor.hasName[J. Carrey])

  15. Outline of this presentation • AVATAR: A TV recommender system. • User Modelling based on ontologies • Updating user profiles • Conclusions and Further Work

  16. 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)

  17. 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

  18. Degree of Interest (II) Old DOI of instance Instk(before updating) New DOI of instance Instk (after updating) The index of a class is computed by adding the contribution of each instance of that class

  19. 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

  20. 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

  21. Relevance index Relevance (C) 1 User choices C3 C2 C1 Scenario 1 -1 Scenario 2 Scenario 3

  22. Outline of this presentation • AVATAR: A TV recommender system • User Modelling based on ontologies • Updating user profiles • Conclusions and Further Work

  23. Conclusions • Ontology-profiles favour inferential processes to improve the offered suggestions • Indexes flexible enough to maintain the user preferences permanently updated

  24. 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)

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