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Personalized Information Retrieval in Context
David ValletUniversidad Autónoma de Madrid, Escuela Politécnica Superior,Spain
Overview
MotivationOntology-Based Content RetrievalPersonalizationPersonalization in Context
Building a Semantic Runtime Context Contextual Preference Activation
Conclusions
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)
Ontology-Based Content Retrieval
Infoneed
Formalquery
Query enginesInference engines
Ontology KB
Annotation
Documents
Searchspace
Returneddocuments
Ranking ?
Goal: Improve keyword-based search
2
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x2
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similarity , cosd q
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Ontology-Based Content Retrieval
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x2 q2 d12 d22
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Ontology
Query q
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Documents
Users
Personalization
Ontology KB
Annotation
Documents
Searchspace
Preferences/Context
Personalization
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Personalization effect
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Personalization
Concepts VS Keywords Interoperability Precision Hierarchical Representation Inference
Ontology-Based Preference Representation
Personalization
C TopicsC PoliticsC SportsC Leisure
C Travel
C MoviesC Music
C TechnoC Classical
C Island Travel
C Political Region
C USA
C AmericaC NorthAmerica
C Canada
I Hawaii
C USA Islands
C Geographical RegionC Islands
C Region
locatedIn
visit
C Florida
C Spanish Islands
C Pop
Hawaii Tourist Guide
Ontology-Based Preference Representation
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
Building a Runtime Context
11
ContexttContextt
Concepts, t’
ActionQuery
ActionQuery
Content viewed
Content modified
Query
Visualquery
Textualquery
Visualfeedback
Contentannotations
Queryconcepts
Concept average
concepts
ActionQuery
ActionQuery
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t
Contextual Preference Activation
preference for x = px
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px
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Domain ontology
Domain ontology
Constrained Spreading Activation
C C
needs
Boat
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Initial runtime context
Contextt
Initial user preferences
Semanticuser preferences
Extended user preferences Extended context
Domainconcepts
Contextualiseduser preferences
Contextual Preference Activation
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Personalization in Context
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Conclusions
Semantic concepts VS plain termsExploitation of semantic relationSemantic runtime contextContext: Filtering of user preference
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.
Thank You!