Enhancing content-based recommendation with the task model of classification

Y. Wang, S. Wang, N. Stash, L.M. Aroyo, A.Th. Schreiber, Shenghui Wang

    Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

    13 Citaten (Scopus)

    Samenvatting

    In this paper, we define reusable inference steps for content-based recommender systems based on semantically-enriched collections. We show an instantiation in the case of recommending artworks and concepts based on a museum domain ontology and a user profile consisting of rated artworks and rated concepts. The recommendation task is split into four inference steps: realization, classification by concepts, classification by instances, and retrieval. Our approach is evaluated on real user rating data. We compare the results with the standard content-based recommendation strategy in terms of accuracy and discuss the added values of providing serendipitous recommendations and supporting more complete explanations for recommended items.
    Originele taal-2Engels
    TitelKnowledge Engineering and Management by the Masses (17th International Conference, EKAW 2010, Lisbon, Portugal, October 11-15, 2010. Proceedings)
    RedacteurenP. Cimiano, H.S. Pinto
    Plaats van productieBerlin
    UitgeverijSpringer
    Pagina's431-440
    ISBN van geprinte versie978-3-642-16437-8
    DOI's
    StatusGepubliceerd - 2010

    Publicatie series

    NaamLecture Notes in Computer Science
    Volume6317
    ISSN van geprinte versie0302-9743

    Vingerafdruk

    Duik in de onderzoeksthema's van 'Enhancing content-based recommendation with the task model of classification'. Samen vormen ze een unieke vingerafdruk.

    Citeer dit