Diversifying movie recommendations is an effective way to address choice overload, a phenomenon where recommenders generate lists with highly similar recommendations that are difficult to choose from. However, existing diversification algorithms often rely on latent features, which limits their interpretability and makes it less clear why a particular set of movies is recommended. Given that movies are designed to elicit emotional responses, researchers have suggested leveraging these responses to enhance recommender system performance. This study introduces a novel “emotion diversification” approach, which diversifies movie recommendations based on emotional signals extracted from audience reviews. We evaluate this method against latent and non-diversified baselines in a controlled user study (N = 115), finding that it significantly improves perceived taste coverage and system satisfaction without compromising recommendation quality. Going beyond the traditional rating- and/or interaction data used by traditional recommender systems, our work demonstrates the user experience benefits of extracting emotional data from rich, qualitative user feedback and using it to give users a more emotionally diverse set of recommendations.

With the pervasive and evolving use of tracking and AI to make inferences about online platform users, it has become imperative for adolescents—a key demographic using such platforms—to develop a deep understanding of these practices to protect their privacy. Traditionally, K-12 cybersecurity education has largely been confined to extracurricular activities, limiting underrepresented students’ access. To resolve this shortcoming, we partnered with a rural-identifying middle school to deliver AI-related privacy education in classrooms. Using Design-Based Research methodology, we identified students’ AI-related privacy learning needs and developed six education modules. This paper focuses on the design, classroom implementation, and evaluation of module #2, covering the privacy threats of Tracking and Pervasive Personalization (TaPP). Student assessment outcomes show they developed transferable foundational knowledge of the privacy implications of tracking and personalization after participating in the TaPP module. Our findings demonstrate the benefits of integrating AI-related privacy education into existing K-12 curricula.