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

Aug 4, 2026

Mentoring relationships provide a structured platform for personal and professional growth in modern business settings. This study examines mentorship programs across high school, university, and workplace contexts, emphasizing alignment of mentors and mentees by shared interests, expertise, and goals. Learning and teaching style compatibility is treated as critical for productive relationships. A hybrid machine learning approach combines collaborative filtering and content-based filtering to recommend mentor-mentee pairs using skills, goals, and personality traits to improve communication and mentorship outcomes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Introduction

Mentorship across life stages

The matching process

The purpose

Learning and teaching style considerations

Personality-based Hybrid Machine Learning Model for Mentor-Mentee Matching using Collaborative and Content Filtering Methods 1st Jitty Varghese Engineering, Computer & Mathematical Sciences Auckland University of Technology Auckland, New Zealand [wpf2576@autuni.ac.nz](wpf2576@autuni.ac.nz) 2nd Mahsa Mohaghegh Engineering, Computer & Mathematical Sciences Auckland University of Technology Auckland, New Zealand [mahsa.mohaghegh@aut.ac.nz](mahsa.mohaghegh@aut.ac.nz) 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. Keywords—mentor, mentorship, matching algorithm, machine learning, content-based filtering, collaborative filtering I. INTRODUCTION In the contemporary world of business, the establishment of strong mentoring relationships is deemed indispensable as they serve as a valuable platform for personal and professional growth [1]. Acknowledging the inherent worth of mentorship for both mentors and mentees holds great significance in such a dynamic work environment. By fostering a mentoring culture, organizations can actively encourage collaboration, knowledge sharing, and continuous learning. This preface sets the stage fora comprehensive exploration of the considerable significance and advantageous impacts that mentoring relationships hold within the workplace. The mentorship program plays a crucial role in the development and success of high school students, university students, and individuals in the workplace. While the mentorship programs for each group differ in focus, they all serve the purpose of guiding and supporting mentees towards their respective goals. In high school, mentorship programs are designed to assist students in making decisions about their future after highschool [2]. They provide guidance in career exploration, college or vocational training choices, and personal development. It is important to highlight the valuable insightsand advice that mentorsin high school programs can offer to help students navigate through this significant transitional period. For university students, mentorship programs have a more focused approach towards academic and professional growth [3]. Mentors can aid in course selection, major declaration, and exploring internship opportunities. It is crucial to emphasize the importance of mentors in helping students make informed decisions about their academic and career paths, as well as providing guidance on networking and developing necessary skills. In the workplace, mentorship programs aim to support individuals as they navigate their careers and strive for professional growth [4]. Mentors in these programs provide guidance on job-specific skills, industry knowledge, and career ad

FAQs

Why is mentorship considered important in the business and education contexts discussed?

Mentorship supports personal and professional growth and helps mentors and mentees benefit through guidance, knowledge sharing, and continuous learning.

What problem does the paper address regarding mentor-mentee pairing?

The paper highlights that personal factors such as learning/teaching styles and personality traits are often overlooked, and effective pairing needs consideration of interests, expertise, and goals.

How does the proposed hybrid model recommend mentor-mentee matches?

It combines collaborative filtering with content-based filtering, using skills and goals and incorporating personality traits to determine compatibility and communication styles.

Aug 4, 2026

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