The seven layers of complexity of recommender systems for children in educational contexts

Journal Article (2019)
Author(s)

Emiliana Murgia (UniversitĂ  degli Studi di Milano Bicocca)

Monica Landoni (University of Lugano)

Theo Huibers (University of Twente)

Jerry Alan Fails (Boise State University)

Maria Soledad Pera (Boise State University)

Affiliation
External organisation
More Info
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Publication Year
2019
Language
English
Affiliation
External organisation
Volume number
2449
Pages (from-to)
5-9

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.

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