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

Research output: Contribution to conferencePaperResearchpeer-review

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.
Original languageEnglish
Publication statusPublished - 14 Aug 2018
Event27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy
Duration: 22 Oct 201826 Oct 2018

Conference

Conference27th ACM International Conference on Information and Knowledge Management, CIKM 2018
CountryItaly
CityTorino
Period22/10/1826/10/18

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Recommender systems
Software architecture
Metadata

Keywords

  • cs.IR

Cite this

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

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

2018. Paper presented at 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy.

Research output: Contribution to conferencePaperResearchpeer-review

Kowald, D, Lacic, E, Theiler, D & Lex, E 2018, 'AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments' Paper presented at 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, 22/10/18 - 26/10/18, .
Kowald D, Lacic E, Theiler D, Lex E. AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments. 2018. Paper presented at 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy.
Kowald, Dominik ; Lacic, Emanuel ; Theiler, Dieter ; Lex, Elisabeth. / AFEL-REC : A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments. Paper presented at 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy.
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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 - Lex, Elisabeth

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N2 - 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.

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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