PassionPulse: Personalized Hobby Exploration and Suggestion System

In today’s fast-paced world, individuals often struggle to explore and pursue their personal interests and hobbies due to busy schedules and a lack of self-awareness regarding their preferences. PassionPulse: Personalized Hobby Exploration and Suggestion System aims to bridge this gap by leveraging a data-driven approach to recommend hobbies based on user responses. The system collects user inputs through a structured questionnaire, applies an internal mapping mechanism to analyze responses, and suggests personalized hobby recommendations. By integrating web technologies such as HTML, CSS, JavaScript, Bootstrap, and Spring Boot, along with MySQL for data management, the system ensures seamless functionality and user experience. Unlike conventional recommendation models, PassionPulse prioritizes a personalized and interactive approach, enabling users to discover hobbies aligned with their interests. The study explores the effectiveness of PassionPulse through user engagement and recommendation accuracy, highlighting its potential in self-discovery, mental well-being, and skill development. Additionally, the system presents future scalability options, including a hobby purchase feature to provide users with structured guidance for pursuing their interests. With a growing need for personalized recommendation systems in various domains, this research contributes to enhancing hobby exploration through intelligent, user-centric methodologies.