A Hybrid Framework for Personalized Scheduling Using Natural Language Processing and Collaborative Filtering

In an era characterized by an abundance of digital information, the task of sifting through a vast array of events and appointments to find personalized and relevant scheduling solutions has become increasingly complex. In today’s fast-paced, information-overloaded world, managing schedules efficiently remains a critical challenge. Traditional calendar systems often fail to accommodate personalized scheduling needs, leaving room for inefficiencies. This study aims to address this gap by developing an NLP-Enhanced Smart Calendar that leverages natural language processing (NLP) techniques for accurate, tailored scheduling. The objective of this study is to create a smart calendar system that integrates user preferences, historical scheduling patterns, and event categories to generate personalized scheduling recommendations using NLP and collaborative filtering algorithms. The proposed system integrates user preferences, historical scheduling patterns, and event categories to generate personalized scheduling recommendations. A combination of collaborative filtering and NLP algorithms continuously refines these suggestions. The system adapts dynamically to evolving user preferences and scheduling patterns.