1. AVATAR An Improved Solution for Personalized TV based on Semantic Inference Yolanda Blanco Fern á ndez, Jos é J. Pazos Arias, Mart í n L ó pez Nores, Alberto Gil Solla, Manuel Ramos Cabrer bearhsu 20060425

2. outline Introduction Related Work The AVATAR Recommend System Example Conclusion

3. outline Introduction Related Work The AVATAR Recommend System Example Conclusion

4. Introduction DTV generation Huge number of channels & contents will cause users to be disoriented A personal assistant is required To know what ’ s available and how to find them To furnish a highly-personalized viewing experience

5. outline Introduction Related Work The AVATAR Recommend System Example Conclusion

6. Related Work Recommender => suggestions according to users ’ preferences & needs Hot in the last 2 decades in both TV domain and outside of it Recommender Systems Content-based Collaborative filtering

7. Content-based methods Quantify the similarity between users ’ profiles & programs ’ candidates To define appropriate descriptions of the considered contents Usually a time consuming task user program quantify Suggestion / Similarity

8. Drawbacks Limited diversity while recommending Maybe always suggest from few programs Suggestions based on immature profiles to new users Content-based methods

9. Collaborative approaches More diverse recommendations Based on users with similar preferences Search correlations among the ratings from users Resource-demanding content descriptions aren ’ t necessary Movielens, Moviefinder

10. Collaborative approaches Drawbacks A significant latency observed Requires that users have watched and rated a specific content for it A meaningful number of users is required Sparsity problem #programs increasing, 2 users hardly watch the same program Hampers the discovery of like-minded users

11. More … Hybrid approaches PTV & PTVPlus Semantic inference AVATAR Improve recommending quality due to semantic inference

12. outline Introduction Related Work The AVATAR Recommend System Example Conclusion

13. AVATAR System Advanced Telematic search of Audiovisual contents by semantic Reasoning AVATAR designing byelaws: Broadcast through a TV service Adopt normalized formats & tech ’ s MHP, TV-Anytime Allows adding new personalization tech ’ s & adopting future standards

14. AVATAR System OWL language TV-Anytime Normalize a common data format to describe TV contents

15. AVATAR System – an excerpt

16. AVATAR System – user profile User ’ s profile => hierarchical structure programs the user likes along with their attributes identified by instances, classes and properties formalized in the OWL ontology

17. AVATAR System - DOI Assign an index to each class/instance DOI (Degree of Interest) DOI is computed depends on: Accepted or rejected by user Percentage of the program watched How long to decide to watch this program

18. AVATAR System - flow user program Content-based strategy Collaborative stratesy Final Recommendation

19. Content-based Strategy Hierarchical Semantic Similarity Fine the common ancestor If the nearest ancestor is the root, their similarity is null Inferential Semantic Similarity Discovering implicit relations between 2 The greater the number of common instances, the higher the inferential similarity value - Semantic similarity

20. Collaborative Strategy Goal – find “ neighbors ” having same preferences Define rating vectors DOI indexes for classes of TV contents Alleviates sparsity problem Compute Pearson-r between users neighborhood constructed AVATAR checks if the target content is appealing for the neighbors Predicted value is greater when: Target is appealing for the neighbors The neighbors ’ preferences are strongly correlated - Semantic prediction 線性相關係數

21. Final Recommendation “ two-chance ” mechanism Target content User Profile Semantic Value > β Match y n suggest Semantic prediction > β Match y suggest n discard

22. AVATAR System - Architecture Feedback Agent: Modify DOI indexes in user ’ s profile according to user ’ s response while watching

23. outline Introduction Related Work The AVATAR Recommend System Example Conclusion

24. Example Target content: Dancing with the Stars Target user: U Neighbors:N 1 => N 3

25. OWL ontology (subset)

26. AVATAR System – an excerpt

27. AVATAR System - Recommend

28. outline Introduction Related Work The AVATAR Recommend System Example Conclusion

29. Presented a hybrid recommendation strategy for a TV intelligent assistant Reduces the sparsity problem of the collaborative filtering approaches alleviates the lack of diversity associated to content-based methods Semantic similarity Future work Continue the experimental evaluation Compare with more traditional approaches