A Recommendation Cascade for e-learning

James Buncle, Rachid Anane, Minoru Nakayama

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

    5 Citations (Scopus)
    40 Downloads (Pure)


    This paper is concerned with the presentation of a collaborative recommendation system that implementsa cascade of strategies in order to support the learning process. Similarities between learners are determined by taking advantage of the underlying implicit or explicit personalisation and of the non-personalised modes of interaction. In the personalised approach implicit profiles are based on the patterns of behaviour of learners, while explicit profiles are generated from the results of a questionnaire on learning style. The non-personalisation approach relies on the cumulative intervention of a community of learners implied by the recorded frequency of the usage of objects by learners, and by the expert rating of objects by teachers. Content-based and collaborative approaches are combined into a hybrid model that widens the range of objects to which a learner may be exposed. The quality of service of the recommendation system is evaluated by considering the accuracy of its predictive capability on a publicly available data set.
    Original languageEnglish
    Title of host publicationThe 27th IEEE International Conference on Advanced Information Networking and Applications (AINA-2013)
    Number of pages8
    ISBN (Electronic)978-0-7695-4953-8
    ISBN (Print)978-1-4673-5550-6
    Publication statusPublished - 25 Mar 2013
    Event27th International Conference on Advanced Information Networking and Applications - Barcelona, Spain
    Duration: 25 Mar 201328 Mar 2013
    Conference number: 27

    Publication series

    ISSN (Print)1550-445X
    ISSN (Electronic)1550-445X


    Conference27th International Conference on Advanced Information Networking and Applications
    Abbreviated titleWAINA 2013

    Bibliographical note

    © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


    • recommendation
    • personalisation
    • explicit profile
    • implicit profile
    • neighbourhood


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