Dynamic Personalization of Multimedia Content Based on User Model

Abstract

This project investigates the potential of personalized interactions with social robots, like Pepper, by customizing images and videos to align with individual user profiles. The primary goal is to make conversations with social robots more engaging and relatable. By tailoring content to factors such as age, gender, interests, and cultural background, we aim to enhance user satisfaction and foster broader acceptance of social robots in everyday settings. This study opens up new possibilities for designing robots that can adapt to diverse user needs and preferences. Ultimately, this approach could lead to social robots that are more widely accepted and valued in a range of contexts, from customer service to education to healthcare. This project provides a path for future research in context-aware robot communication.