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User Recommender based on information from forums

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Published:10 September 2014Publication History

ABSTRACT

This paper proposes: a tool for virtual learning platforms that recommends users who know about a particular subject; a new recommender method that takes into account the users' knowledge about a subject and their future availability based on past interactions and a text mining method to obtain user information from forums. A user recommender system was implemented in the Moodle platform to demonstrate the feasibility of the proposal.

References

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  2. Gaudioso, E.; Hernandez-del-Olmo, F.; Montero, M. "Enhancing E-Learning Through Teacher Support: Two Experiences", Education, IEEE Transactions on, On page(s): 109--115 Volume: 52, Issue: 1, Feb. 2009 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Zhuhadar, L.; Romero, E.; Wyatt, R. "The Effectiveness of Personalization in Delivering E-learning Classes", Advances in Computer-Human Interactions, 2009. Second International Conferences on, On page(s): 130--135 Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Salehi, M.; Kmalabadi, I.N. "Attribute-based recommender system for learning resource by learner preference tree", Computer and Knowledge Engineering (ICCKE), 2012 2nd International eConference on, On page(s): 133--138Google ScholarGoogle Scholar
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  • Published in

    cover image ACM Other conferences
    Interacción '14: Proceedings of the XV International Conference on Human Computer Interaction
    September 2014
    435 pages
    ISBN:9781450328807
    DOI:10.1145/2662253

    Copyright © 2014 Owner/Author

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 10 September 2014

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    Qualifiers

    • research-article
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate109of163submissions,67%

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