Personalized information retrieval in context

Vallet, D; Fernandez, M; Castells, P; Mylonas, P; Avrithis, Y

dc.contributor.author	Vallet, D	en
dc.contributor.author	Fernandez, M	en
dc.contributor.author	Castells, P	en
dc.contributor.author	Mylonas, P	en
dc.contributor.author	Avrithis, Y	en
dc.date.accessioned	2014-03-01T02:44:09Z
dc.date.available	2014-03-01T02:44:09Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31710
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-33847618728&partnerID=40&md5=2708d033e1f23408128b5be866e2b01f	en
dc.relation.uri	http://www.aaai.org/Papers/Workshops/2006/WS-06-12/WS06-12-007.pdf	en
dc.relation.uri	http://www.acemedia.org/aceMedia/files/document/wp7/2006/mrc06-uam-iti.pdf	en
dc.relation.uri	https://www.aaai.org/Papers/Workshops/2006/WS-06-12/WS06-12-007.pdf	en
dc.subject	Complex Multiplication	en
dc.subject	Information Retrieval	en
dc.subject	User Preferences	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Context sensitive languages	en
dc.subject.other	Human computer interaction	en
dc.subject.other	Ontology	en
dc.subject.other	Semantics	en
dc.subject.other	Content retrieval	en
dc.subject.other	Retrieval tasks	en
dc.subject.other	Semantic contexts	en
dc.subject.other	Subsets	en
dc.subject.other	Content based retrieval	en
dc.title	Personalized information retrieval in context	en
heal.type	conferenceItem	en
heal.publicationDate	2006	en
heal.abstract	Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in such a way that out of context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context. copyright © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.	en
heal.journalName	AAAI Workshop - Technical Report	en
dc.identifier.volume	WS-06-12	en
dc.identifier.spage	28	en
dc.identifier.epage	32	en