Probabilistic learning for selective dissemination of information

G. Amati, F. Crestani

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

New methods and new systems are needed to filter or to selectively distribute the increasing volume of electronic information being produced nowadays. An effective information filtering system is one that provides the exact information that fulfills user's interests with the minimum effort by the user to describe it. Such a system will have to be adaptive to the user changing interest. In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalized probabilistic model of information retrieval. The model is based on the concept of 'uncertainty sampling', a technique that allows for relevance feedback both on relevant and nonrelevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile.
LanguageEnglish
Pages633-654
Number of pages21
JournalInformation Processing and Management
Volume35
Issue number5
Publication statusPublished - 1999

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Information filtering
learning
information system
Information retrieval
information retrieval
Sampling
Feedback
uncertainty
electronics
Dissemination
Learning model

Cite this

Amati, G. ; Crestani, F. / Probabilistic learning for selective dissemination of information. In: Information Processing and Management. 1999 ; Vol. 35, No. 5. pp. 633-654.
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Probabilistic learning for selective dissemination of information. / Amati, G.; Crestani, F.

In: Information Processing and Management, Vol. 35, No. 5, 1999, p. 633-654.

Research output: Contribution to journalArticle

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