1. Personalized Information Retrieval in Context David Vallet Universidad Autónoma de Madrid, Escuela Politécnica Superior,Spain

2. Overview  Motivation  Ontology-Based Content Retrieval  Personalization  Personalization in Context  Building a Semantic Runtime Context  Contextual Preference Activation  Conclusions

3. Motivation  Indicate user’s preferences  Content  High level: Topics  Low level:  Topic sub-categories  Geographical area  Personalised content  Search results  Browsing  Context awareness  Temporal preference  Different scopes  Session focused interests Ontology-Based Preference Representation Personalisation in Context  Requirements of two different multimedia applications european research projects: digital album (aceMedia) and a news service (MESH)

4. Ontology-Based Content Retrieval Info need Formal query Query engines Inference engines Ontology KB Annotation Documents Search space Returned documents Ranking ? Goal: Improve keyword-based search

5. x3x3 x1x1 x2x2 {x 1, x 2, x 3 } = domain ontology O Ontology-Based Content Retrieval qd1d1 d2d2 x1x1 q1q1 d 11 d 21 x2x2 q2q2 d 12 d 22 x3x3 q3q3 d 13 d 23 x1x1 x2x2 x3x3 Ontology Query q d2d2 d1d1 Documents

6. Users Personalization Ontology KB Annotation Documents Search space Preferences/Context

7. Personalization x3x3 x1x1 x2x2 {x 1, x 2, x 3 } = domain ontology O α2α2 α1α1 Personalization effect

8. Personalization  Concepts VS Keywords  Interoperability  Precision  Hierarchical Representation  Inference Ontology-Based Preference Representation

9. Personalization C Topics C Politics C Sports C Leisure C Travel C Movies C Music C Techno C Classical C Island Travel C Political Region C USA C America C NorthAmerica C Canada I Hawaii C USA Islands C Geographical Region C Islands C Region locatedIn visit C Florida C Spanish Islands C Pop Hawaii Tourist Guide Ontology-Based Preference Representation

10. Personalisation in Context  Combination of long-term (preferences) + short-term (context) user interests and needs  Not all user preferences are relevant all the time: which ones?  Partial answer: focus on current semantic context, discard out of context ones  Notion of context  Defined as the set of background themes under which user activities occur within a given unit of time  Represented as a set of weighted ontology concepts involved in user actions within a session  Captured?  Build a runtime context: extracting concepts from queries and documents selected by the user  Used?  Contextual preference activation: Analyze semantic connections between preference and context concepts  Personalization retrieval in context: Filter user preferences, only those related to the context are activated

11. Building a Runtime Context 11 Context t Concepts, t’ Action Query Action Query Content viewed Content modified Query Visual query Textual query Visual feedback Content annotations Query concepts Concept average concepts Action Query Action Query Context t t

12. Contextual Preference Activation preference for x = p x r (x,y) Beach x Sea y nextTo r p x 0.8 p y 0.4 = 0.8  0.5 w (r) 0.5  preference for y = p x · w (r) p y 0.724 = 0.4 + (1 - 0.4)  0.9  0.6 Domain ontology Constrained Spreading Activation C C needs Boat 0.6 0.9 C

13. Initial runtime context Context t Initial user preferences Semantic user preferences Extended user preferences Extended context Domain concepts Contextualised user preferences Contextual Preference Activation

14. Personalization in Context x3x3 x1x1 x2x2 {x 1, x 2, x 3 } = domain ontology O α2α2 α1α1 α’2α’2 α’1α’1

15. Conclusions  Semantic concepts VS plain terms  Exploitation of semantic relation  Semantic runtime context  Context: Filtering of user preference

16. References  Semantic Search  P. Castells, M. Fernández, and D. Vallet. An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval. IEEE Transactions on Knowledge and Data Engineering, 2007. In press.  Personalization  D. Vallet, P. Mylonas, M. A. Corella, J. M. Fuentes, P. Castells, and Y. Avrithis. A Semantically- Enhanced Personalization Framework for Knowledge-Driven Media Services. IADIS WWW/Internet Conference (ICWI 2005). Lisbon, Portugal, October 2005.  Personalization in context  D. Vallet, M. Fernández, P. Castells, P. Mylonas, and Y. Avrithis. Personalized Information Retrieval in Context. 3rd International Workshop on Modeling and Retrieval of Context (MRC 2006) at the 21st National Conference on Artificial Intelligence (AAAI 2006). Boston, USA, July 2006.  Ranking Aggregation  M. Fernndez, D. Vallet, and P. Castells. Using Historical Data to Enhance Rank Aggregation. 29th Annual International ACM Conference on Research and Development on Information Retrieval (SIGIR 2006), Poster Session. Seattle, WA, August 2006.  Tuning Personalization  P. Castells, M. Fernndez, D. Vallet, P. Mylonas, and Y. Avrithis. Self-Tuning Personalized Information Retrieval in an Ontology-Based Framework. 1st IFIP WG 2.12 & WG 12.4 International Workshop on Web Semantics (SWWS 2005), November 2005. Springer Verlag Lecture Notes in Computer Science, Vol. 3762. Meersman, R.; Tari, Z.; Herrero, P. (Eds.), 2005, ISBN: 3-540-29739-1, pp. 977-986.

17. Thank You!