The Seven Layers of Complexity of Recommender Systems for Children in Educational Contexts

Emiliana Murgia, Monica Landoni, Theo W.C. Huibers, Jerry Alan Fails, Maria Soledad Pera

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

2 Citations (Scopus)
12 Downloads (Pure)

Abstract

Recommender systems (RS) in their majority focus on an average target user: adults. We argue that for non-traditional populations in specific contexts, the task is not as straightforward–we must look beyond existing recommendation algorithms, premises for interface design, and standard evaluation metrics and frameworks. We explore the complexity of RS in an educational context for which young children are the target audience. The aim of this position paper is to spell out, label, and organize the specific layers of complexity observed in this context.
Original languageEnglish
Title of host publicationProceedings of the Workshop on Recommendation in Complex Scenarios
PublisherAssociation for Computing Machinery (ACM)
Pages5-9
Number of pages5
Publication statusPublished - 22 Sep 2019
EventWorkshop on Recommendation in Complex Scenarios 2019 - Scandic Falkoner hotel, Copenhagen, Denmark
Duration: 20 Sep 201920 Oct 2019
https://recsys.acm.org/recsys19/complexrec/

Publication series

NameCEUR Workshop Proceedings
Volume2449
ISSN (Electronic)1613-0073

Workshop

WorkshopWorkshop on Recommendation in Complex Scenarios 2019
CountryDenmark
CityCopenhagen
Period20/09/1920/10/19
Internet address

Keywords

  • children
  • recommender systems
  • education
  • roles
  • guidance
  • interface
  • algorithm
  • teachers

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