Prediction strategies in a TV recommender system - Method and experiments

M.J. van Setten, M. Veenstra, Elisabeth M.A.G. van Dijk, Antinus Nijholt

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Abstract

    Predicting the interests of a user in information is an important process in personalized information systems. In this paper, we present a way to create prediction engines that allow prediction techniques to be easily combined into prediction strategies. Prediction strategies choose one or a combination of prediction techniques at the moment a prediction is required, taking into account the most up -to-date knowledge about the current user, other users, the information and the system itself. Results of two experiments show that prediction strategies improve both the accuracy and stability of prediction engines. One of these experiments involves a TV recommender system. This paper describes the method of prediction strategies, how they have been applied in the TV recommender system and results of the experiment in detail.
    Original languageUndefined
    Title of host publicationProceedings IADIS International Conference WWW/Internet 2003
    EditorsP. Isaísas, N. Karmakar
    Place of PublicationLisbon, Portugal
    PublisherIADIS
    Pages203-210
    Number of pages8
    ISBN (Print)972-98947-1-X
    Publication statusPublished - 5 Nov 2003
    EventIADIS International Conference WWW/Internet 2003 - Algarve, Portugal
    Duration: 5 Nov 20038 Nov 2003

    Publication series

    Name
    PublisherIADIS

    Conference

    ConferenceIADIS International Conference WWW/Internet 2003
    CountryPortugal
    CityAlgarve
    Period5/11/038/11/03

    Keywords

    • Recommender Systems
    • Personalization
    • User Modeling
    • METIS-217440
    • Adaptive Systems
    • IR-64050
    • HMI-IE: Information Engineering
    • EWI-9965

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