Mentor Connect Using Hybrid Collaborative Filtering For Personalized Matching

Abstract

Mentor Connect is an intelligent matchmaking platform designed to foster meaningful mentor-         mentee relationships by leveraging hybrid collaborative filtering for personalized pairing.        Traditional mentoring systems often rely on manual matching or simplistic criteria, resulting in        suboptimal outcomes due to the lack of adaptability and personalization. To address this, Mentor Connect integrates both user-based and item-based collaborative filtering with content  -based features to enhance the accuracy and relevance of recommendations. The hybrid model captures implicit and explicit user preferences, such as areas of interest, professional        background, interaction history, and feedback, to dynamically learn and evolve matching strategies. By combining behavioral data with profile attributes, the system identifies latent       patterns and complementary skill sets, enabling more effective and enduring mentor-mentee connections. Evaluation results show improved user satisfaction and engagement,      demonstrating the mod   el's potential to scale across diverse domains w here personalized      mentorship is critical. Mentor Connect represents a significant step toward data-driven mentorship, emphasizing adaptability, scalability, and human-centric design.