Personality-Based Hybrid Machine Learning Model for Mentor-Mentee Matching Using Collaborative and Content Filtering Methods

Abstract

Mentoring relationships have gained increasing significance in the contemporary business world, serving as a valuable platform for personal and professional growth. This study endeavors to explore the importance and impacts of mentorship relationships within the workplace. It investigates the role of mentorship programs in high school, university, and workplace settings, with an emphasis on the cruciality of aligning mentors and mentees based on shared interests, expertise, and goals. Consideration of factors such as learning and teaching styles becomes essential to cultivate a productive mentor-mentee relationship. To facilitate the identification of suitable mentor-mentee pairings based on skills, goals, and personality types, this study presents a hybrid machine learning model that combines collaborative filtering and content-based filtering algorithms. The analysis of skills and goals aids mentors in guiding mentees in their professional development, while evaluating personality traits helps determine compatibility and communication styles. In conclusion, this study suggests leveraging machine learning algorithms to recommend mentors based on various factors, utilizing personality types as one of the attributes to pair the most compatible mentor and mentee, ultimately leading to successful mentorship programs.