Multi-topic information filtering with a single user profile

Nikolaos Nanas, Victoria Uren, Anne de Roeck, John Domingue

Research output: Chapter in Book/Published conference outputConference publication


In Information Filtering (IF) a user may be interested in several topics in parallel. But IF systems have been built on representational models derived from Information Retrieval and Text Categorization, which assume independence between terms. The linearity of these models results in user profiles that can only represent one topic of interest. We present a methodology that takes into account term dependencies to construct a single profile representation for multiple topics, in the form of a hierarchical term network. We also introduce a series of non-linear functions for evaluating documents against the profile. Initial experiments produced positive results.
Original languageEnglish
Title of host publicationMethods and applications of artificial intelligence
Subtitle of host publicationthird Hellenic conference on AI, SETN 2004, Samos, Greece, May 5-8, 2004. Proceedings
EditorsGeorge A. Vouros, Themistoklis Panayiotopoulos
Place of PublicationBerlin (DE)
Number of pages10
ISBN (Electronic)978-3-540-24674-9
ISBN (Print)978-3-540-21937-8
Publication statusPublished - 2004
Event3rd Hellenic Conference on Artificial Intelligence - Samos, Greece
Duration: 5 May 20048 May 2004

Publication series

NameLecture notes in computer science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd Hellenic Conference on Artificial Intelligence
Abbreviated titleSETN 2004


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