Layered evaluation of multi-criteria collaborative filtering for scientific paper recommendation

N. Manouselis, K. Verbert

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

    14 Citations (Scopus)
    104 Downloads (Pure)


    Recommendation algorithms have been researched extensively to help people deal with abundance of information. In recent years, the incorporation of multiple relevance criteria has attracted increased interest. Such multi-criteria recommendation approaches are researched as a paradigm for building intelligent systems that can be tailored to multiple interest indicators of end-users – such as combinations of implicit and explicit interest indicators in the form of ratings or ratings on multiple relevance dimensions. Nevertheless, evaluation of these recommendation techniques in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined that the performance of such algorithms is often dependent on the dataset – and indicate the importance of carrying out careful testing and parameterization. Especially when looking at large scale datasets, it becomes very difficult to deploy evaluation methods that may help in assessing the effect that different system components have to the overall design. In this paper, we study how layered evaluation can be applied for the case of a multi-criteria recommendation service that we plan to deploy for paper recommendation using the Mendeley dataset. The paper introduces layered evaluation and suggests two experiments that may help assess the components of the envisaged system separately. Keywords: Recommender systems; Multi-Criteria Decision Making (MCDM); Evaluation
    Original languageEnglish
    Title of host publication13th International Conference on Computational Science (ICCS 2013, Barcelona, Spain, June 5-7, 2013)
    EditorsV. Alexandrov, M. Lees, V. Krzhizhanovskaya, J. Dongarra, P.M.A. Sloot
    Publication statusPublished - 2013

    Publication series

    NameProcedia Computer Science
    ISSN (Print)1877-0509

    Fingerprint Dive into the research topics of 'Layered evaluation of multi-criteria collaborative filtering for scientific paper recommendation'. Together they form a unique fingerprint.

    Cite this