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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Murgia, Emiliana; Landoni, Monica; Huibers, Theo; Fails, Jerry Alan; and Pera, Maria Soledad. (2019). "The Seven Layers of Complexity of Recommender Systems for Children in Educational Contexts". CEUR Workshop Proceedings, 2449, 5-9.