KidRec

Recommender systems (RS) for children are only recently beginning to be studied and are primarily limited to recommenders in education-related environments. When focused on this particular audience, the role of a recommendation system needs to be reformulated, as it is not sufficient for recommenders to identify items that match users’ preferences and interests. Instead, it is imperative that they also explicitly consider children’s needs from multiple perspectives: educational, developmental, and engagement, to name a few. Considering these particular needs opens a rich set of questions to answer:

• What are the benefits of child-specific recommendations?
• What role does personal history, age, developmental stage, or even curricular standards have in RS for children?
• What are the goals of RS for children: persuade, educate, guide, and support their well-being, or something else entirely?
• What are the ethical or privacy challenges associated with this type of recommendation? What are the domains in which recommendations make sense for children? Are they the same as for adults?
• How should we model personalization given that traditional mechanisms of explicit feedback, writing reviews, and purchasing items (directly) do not match this particular population whose literacy levels are growing and who generally do not directly have the ability to purchase items?
• Are their specifics concern, from the point of view of developing technology that should be considered? In addition, the interests and needs of this population is rapidly evolving, as they are still refining their interests as they get to know new things, and enhance their educational context.

The goal of this interactive workshop is to share and discuss research and projects that reach beyond classic recommender techniques and discuss child related challenges of recommender systems. We invite submissions related to recommender systems for children including topics on:

• Technology for children
• Recommender algorithms for children
• Recommendation explanations, transparency,
• Expert-in-the-Loop (e.g., teacher, parent)
• Child-centered content domains (e.g., leisure, education)
• User Profiling
• Personalization
• Gamification
• Ethics
• Privacy