AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments

Publikation: KonferenzbeitragPaperForschungBegutachtung

Abstract

In this paper, we present preliminary results of AFEL-REC, a recommender system for social learning environments. AFEL-REC is build upon a scalable software architecture to provide recommendations of learning resources in near real-time. Furthermore, AFEL-REC can cope with any kind of data that is present in social learning environments such as resource metadata, user interactions or social tags. We provide a preliminary evaluation of three recommendation use cases implemented in AFEL-REC and we find that utilizing social data in form of tags is helpful for not only improving recommendation accuracy but also coverage. This paper should be valuable for both researchers and practitioners interested in providing resource recommendations in social learning environments.
Originalspracheenglisch
PublikationsstatusVeröffentlicht - 14 Aug 2018
Veranstaltung27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italien
Dauer: 22 Okt 201826 Okt 2018

Konferenz

Konferenz27th ACM International Conference on Information and Knowledge Management, CIKM 2018
LandItalien
OrtTorino
Zeitraum22/10/1826/10/18

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Recommender systems
Software architecture
Metadata

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    Kowald, D., Lacic, E., Theiler, D., & Lex, E. (2018). AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments. Beitrag in 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italien.

    AFEL-REC : A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments. / Kowald, Dominik; Lacic, Emanuel; Theiler, Dieter; Lex, Elisabeth.

    2018. Beitrag in 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italien.

    Publikation: KonferenzbeitragPaperForschungBegutachtung

    Kowald D, Lacic E, Theiler D, Lex E. AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments. 2018. Beitrag in 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italien.
    Kowald, Dominik ; Lacic, Emanuel ; Theiler, Dieter ; Lex, Elisabeth. / AFEL-REC : A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments. Beitrag in 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italien.
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    title = "AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments",
    abstract = "In this paper, we present preliminary results of AFEL-REC, a recommender system for social learning environments. AFEL-REC is build upon a scalable software architecture to provide recommendations of learning resources in near real-time. Furthermore, AFEL-REC can cope with any kind of data that is present in social learning environments such as resource metadata, user interactions or social tags. We provide a preliminary evaluation of three recommendation use cases implemented in AFEL-REC and we find that utilizing social data in form of tags is helpful for not only improving recommendation accuracy but also coverage. This paper should be valuable for both researchers and practitioners interested in providing resource recommendations in social learning environments.",
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    author = "Dominik Kowald and Emanuel Lacic and Dieter Theiler and Elisabeth Lex",
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    AU - Lacic, Emanuel

    AU - Theiler, Dieter

    AU - Lex, Elisabeth

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    AB - In this paper, we present preliminary results of AFEL-REC, a recommender system for social learning environments. AFEL-REC is build upon a scalable software architecture to provide recommendations of learning resources in near real-time. Furthermore, AFEL-REC can cope with any kind of data that is present in social learning environments such as resource metadata, user interactions or social tags. We provide a preliminary evaluation of three recommendation use cases implemented in AFEL-REC and we find that utilizing social data in form of tags is helpful for not only improving recommendation accuracy but also coverage. This paper should be valuable for both researchers and practitioners interested in providing resource recommendations in social learning environments.

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