Enhancing content-based recommendation with the task model of classification

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

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

    13 Citations (Scopus)

    Abstract

    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.
    Original languageEnglish
    Title of host publicationKnowledge Engineering and Management by the Masses (17th International Conference, EKAW 2010, Lisbon, Portugal, October 11-15, 2010. Proceedings)
    EditorsP. Cimiano, H.S. Pinto
    Place of PublicationBerlin
    PublisherSpringer
    Pages431-440
    ISBN (Print)978-3-642-16437-8
    DOIs
    Publication statusPublished - 2010

    Publication series

    NameLecture Notes in Computer Science
    Volume6317
    ISSN (Print)0302-9743

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