Strathprints Home | Open Access | Browse | Search | User area | Copyright | Help | Library Home | SUPrimo

Probabilistic learning for selective dissemination of information

Amati, G. and Crestani, F. (1999) Probabilistic learning for selective dissemination of information. Information Processing and Management, 35 (5). pp. 633-654. ISSN 0306-4573

[img]
Preview
PDF (strathprints001880.pdf)
Download (216Kb) | Preview

    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.

    Item type: Article
    ID code: 1880
    Subjects: Bibliography. Library Science. Information Resources > Library Science. Information Science
    Department: Faculty of Science > Computer and Information Sciences
    Related URLs:
      Depositing user: Strathprints Administrator
      Date Deposited: 10 Oct 2006
      Last modified: 16 Mar 2012 16:20
      URI: http://strathprints.strath.ac.uk/id/eprint/1880

      Actions (login required)

      View Item

      Fulltext Downloads: