In scientific conferences, participants are often required to select among parallel presentations based on their interests and logistical constraints, facing challenges related to time management, distances between session rooms, and content overlap. Building a daily schedule that maximizes individual satisfaction thus represents a complex and time-consuming task. In this work, we propose a system based on artificial intelligence and mathematical optimization to assist users in creating an optimal daily program. To this end, we develop a hybrid framework that combines explicit evaluations provided by participants with an automatic estimation of interest indices for talks, leveraging Large Language Models (LLMs). Finally, we present a mobile application, which implements the scheduling optimization process and collects qualitative feedback directly from participants, validating the effectiveness of our approach through experiments conducted on real-world conference data.