Intelligent Career Guidance System for College Students to Sustain in the Emerging Job Sector

Abstract-An advanced career guidance recommendation sys- tem is an innovative algorithm that differentiates itself from traditional recommendation algorithms, which work through a link to provide relevant recommendations and are heavily dependent on user behavior. These algorithms are trained to understand various aspects relating to an applicant—their skills, qualifications, work experience, and preferences—and utilize these data points to create customized recommendations based on their profile and career aspirations. Additionally, these intelligent talent recommendation systems continuously evolve, refining their suggestions by analyzing user feedback and performance insights. This study introduces an innovative approach to talent rec- ommendation specifically designed for college students who are preparing to enter the emerging job sector based on a Rank- Based Sequential Deep Learning (RBS-DL) Model. By studying students’ skills, qualifications, interests, and career aspirations, the algorithm intends to offer personalized recommendations. The effectiveness of the recommendation algorithm enhanced by RBS-DL is assessed through both simulated experiments and empirical verifications. Results show significant improvement in recommendation accuracy and relevance over traditional approaches. For the RBS- DL algorithm, students showed a higher job offer acceptance rate by 40% and around 30% increment in job satisfaction levels. This algorithm also learns from user interactions, adjusting its recommendations over time based on real-time user feedback.