personalized information retrieval in context david vallet universidad autónoma de madrid, escuela...

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

x3

x1

x2

1d

q

similarity , cosd q

1

{x1, x2, x3} = domain ontology

2d

Ontology-Based Content Retrieval

q d1 d2

x1 q1 d11 d21

x2 q2 d12 d22

x3 q3 d13 d23

x1 x2

x3

Ontology

Query q

d2

d1

Documents

Users

Personalization

Ontology KB

Annotation

Documents

Searchspace

Preferences/Context

Personalization

x3

x1

x2

1d��������������

{x1, x2, x3} = domain ontology

2d��������������α2

α1u

Personalization effect

, , , , ,

, ,cos

score d u q f sim d q sim d u

f score d q

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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

Contextt

t

Contextual Preference Activation

preference for x = px

r (x,y)

Beachx

Seay

nextTor

px

0.8

py

0.4 = 0.8 0.5w (r)

0.5

preference for y = px · w (r)

py

0.724 = 0.4 + (1 - 0.4) 0.9 0.6

Domain ontology

Domain ontology

Constrained Spreading Activation

C C

needs

Boat

0.6

0.9

C

Initial runtime context

Contextt

Initial user preferences

Semanticuser preferences

Extended user preferences Extended context

Domainconcepts

Contextualiseduser preferences

Contextual Preference Activation

, ,

,

, , , ,

co , s ' ,

s f sim d qcore d u q t sim d u t

f sim d q

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score d u q f sim d q sim d u

f score d q

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Personalization in Context

x3

x1

x2

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{x1, x2, x3} = domain ontology

2d��������������α2

α1u

α’2α’1

,C u t

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!