Recommendation systems (RS) have traditionally targeted adult users, who can directly and explicitly identify their wants and needs, and are often willing to provide feedback in ther form of ratings and reviews. This is rarely, if at all avaiable from younger populations. As such, existing algorithmic solutions are not responding to children’s needs. RS for children have only recentry began to be studied, and are primarely related to RS in education-related enviroments. When focused on this particular audience, the role of RS needs to be reformulated, as it is not sufficient for RS to identify items that match users’ preferences and interests. Instead, it is imperative they also consider children’s needs from multiple perspectives: educational developmental, and engagement, to name a few. The research agenda established for this area is focused on designing and developing RS that best serve and respond to young users.
Ashlee Milton, Levesson Batista, Garrett Allen, Siqi Gao, Yiu-Kai D Ng, and Maria Soledad Pera. 2020. “‘Don’t Judge a Book by its Cover’: Exploring Book Traits Children Favor”. Short paper in Proceedings of the Fourteenth ACM Conference on Recommender Systems (RecSys ’20). ACM, 6 pp. DOI:10.1145/3383313.3418490.
Ashlee Milton, Michael Green, Adam Keener, Joshua Ames, Michael D Ekstrand, Maria Soledad Pera. 2019. “StoryTime: eliciting preferences from children for book recommendations”. In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys 2019 Demo Proceedings).
Ashlee Milton, Emiliana Murgia, Monica Landoni, Theo Huibers, and Maria Soledad Pera. 2019. “Here, There, and Everywhere: Building a Scaffolding for Children’s Learning Through Recommendations”. In Proceedings of the 1st Workshop on the Impact of Recommender Systems co-located with the 13th ACM Conference on Recommender Systems (ImpactRS 2019).
Emiliana Murgia, Monica Landoni, Theo W.C. Huibers, Jerry Alan Fails, Maria Soledad Pera. 2019. “The Seven Layers of Complexity of Recommender Systems for Children in Educational Contexts”. In Proceedings of the Workshop on Recommendation in Complex Scenarios (CEUR 2019).
Michael Green, Oghenemaro Anuyah, Devan Karsann, MS Pera. 2019. “Evaluating prediction-based recommenders for kids”. In Proceedings of the 3rd International and Interdisciplinary Perspectives on Children & Recommender and Information Retrieval Systems (KidRec 2019), co-located with ACM IDC 2019.
Ion Madrazo Azpiazu, Micahel Green, Oghenemaro Anuyah, and Maria Soledad Pera. 2018. “Can we leverage rating patterns from traditional users to enhance recommendations for children?”. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018 Late-Breaking Results).