AfeL-REc: A recommender system for providing learning resource recommendations in social learning environments

Research output: Contribution to journalConference article

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
Article number46
Number of pages4
JournalCEUR Workshop Proceedings
Volume2482
Publication statusPublished - 1 Jan 2019
Event2018 Conference on Information and Knowledge Management Workshops - Torino, Italy
Duration: 22 Oct 201822 Oct 2018

Keywords

  • Analytics for Everyday Learning
  • Collaborative Filtering
  • Coverage
  • Social Learning Environments
  • Social Recommender Systems

ASJC Scopus subject areas

  • Computer Science(all)

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