1. Web Search Personalization with Ontological User Profile Advisor: Dr. Jai-Ling Koh Speaker: Shun-hong Sie

3. Outline Introduction Web & Search personalization Experimental Conclusion

4. Terminology Query – A search query that comprises of one or more keywords. Context – The representation of a user’s intent for information seeking. Ontology – Explicit specification of concepts and relationships that can exist between them.

5. How do we begin search? 野草莓是怎 麼一回事？ 野草莓 、圓 山、學運

6. For search result Query or localized context Domain knowledge Long-term interests

7. Motivation Combine search technologies and knowledge about query and user context into a single framework in order to provide the most appropriate answer for a user’s information needs.

8. Personalized Search Google (or the other search engine like?!) – Feedback – Re-ranking – Snippets associated – Time consuming

9. Ontologies for web personalization Ontological user profile – Concept are annotated by interest scores derived and updated implicitly on the user’s information access behavior.

10. Ontologies for web personalization Cold-start Match exist concept Collect use behavior Build user’s ontology Maintain and update

11. Representation of Reference Ontology N:total number of document in the training set n i total number of documents that contain term i n: concept

12. Context Model conceptscore

13. Spreading Activation module Assume user behavior can be learned Compute the weights for the relations between each concept and all of its subconcepts using a measure of containment Computer a term vector for each document d i and compare the term vector for d i with the term vectors for concept C j in the user profile.

14. Algorithm 1: Spreading Activation Algorithm

15. Algorithm 2 Algorithm for the Normalization and Updating of Interest Scores in the Ontological User Profile

16. Search Personalization

17. Algorithm 3: Re-ranking Algorithm

18. Experimental Top-n Recall Top-n Precision Data Sets – ODP Training set test set profile set

19. Experimental evaluation User profile convergence – Rate of increase in interest scores stabilizes over incremental updates. Effectiveness of search personalization

20. User profile convergence

21. Average Top-n Recall and Top-n Precision comparisons

22. Standard search with various query sizes

23. CONCLUSIONS Use personal ontology can be used to effectively tailor search results based on users’ interests and preferences.

24. The Norwegian National Knowledge Base SNL Skien coun- cil Cap Lex NBL Henrik Ibsen Hedda Gabler Skien Et dukkehjem A doll’s house wrote born in wrote “reality” topic mapinformation knowledge other topic maps are merged in... Ibsen- centre Et dukkehjem Helmer Dr. Rank Mrs. Linde Krogstad Nora http://www.ontopia.net/ © 2003 Ontopia AS

25. Summary of Core Topic Maps Concepts A pool of information or data –any type or format A knowledge layer, consisting of: knowledge layer information layer Associations –expressing relationships between knowledge topics composed by born in composed by Occurrences –information that is relevant in some way to a given knowledge topic = The TAO of Topic Maps Topics –a set of knowledge topics for the domain in question Puccini Tosca Lucca Madame Butterfly