The Individuality Assisted Estimation Model of Experience

This chapter introduces the Individuality Assisted Estimation model, which explains how audiovisual stimuli and individual factors shape experience ratings. Unlike previous models, the Individuality Assisted Estimation (IAE) model incorporates individual traits and both experience-dependent and -independent temporal states, offering a comprehensive framework for individualized Quality of Experience (QoE) estimation. It hypothesizes that combining individual characteristics with observable reactions and stimuli can enhance the accuracy of QoE models. The chapter begins by discussing the need for individualized metrics to improve service customization and user satisfaction. It then details the development of the model, breaking down the process of forming an experience rating from sensory reception to evaluation, and outlines the components and feedback loops, including stimuli, traits, states, and reactions. The model also incorporates memory through feedback loops to account for past experiences’ impact on current and future reactions. The chapter concludes by discussing the model’s potential applications, emphasizing its benefits for a holistic understanding of QoE, improved predictive accuracy, and enhanced multimedia services. The IAE model aims to provide a robust framework for individualized QoE estimation, leveraging personal data to deliver more accurate and personalized assessments.