Personalized Information Retrieval in Context

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 • Requirements of two different multimedia applications european research projects: digital album (aceMedia) and a news service (MESH) • 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

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

5. Query q x2 Documents d1 x1 x2 d2 x3 Ontology x1 {x1, x2, x3} = domain ontology O x3 Ontology-Based Content Retrieval

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

7. α1 α2 Personalization Personalization effect x2 x1 {x1, x2, x3} = domain ontology O x3

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

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

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 Contextt t Content viewed Content annotations Content modified Visual query Action Query Query Textual query concepts Query concepts Visual feedback Concept average Concepts, t’ Action Query Contextt 11

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

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

14. α1 α’1 α2 α’2 Personalization in Context x2 x1 {x1, x2, x3} = domain ontology O x3

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!