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

8. TV-Anytime Start Trek Long, long time ago, and far, far, far away… fiction space G ES en.......

9. TV ontology structure TV Contents InformativeMovies IncidentsNews EconomyPolitical ActionComedies

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

14. 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])

15. Precedents Rich’s Stereotypes –Groups of users with similar characteristics –Hierarchical organization of stereotypes Middleton’s paper recommender –It uses ontologies to model user’s interests Stock’s AlFresco –Dynamic user modelling based on an activation/inhibition network

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

17. 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)

18. 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

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

20. 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

21. 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

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

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

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

25. 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)