2024 - present

 

Most existing studies on group recommendation systems (GRS), which provide personalized content recommendations to multiple users, have primarily focused on developing algorithms that support efficient decision-making and maximize user satisfaction. However, given that GRS is designed for continuous use by multiple users together, it can also evoke various forms of social interaction beyond mere decision coordination. Despite this potential, little research has examined users’ social experiences with such a system. In light of this, this research investigates the types of social interactions that may emerge as users engage with GRS and explores how this technology can be designed with consideration for such social experiences.