Mentor Recommendation System Using KNN Item-Based Collaborative Filtering

Mentors play a critical part in a person's success.  In today's world, many people, especially students in universities, are struggling to search a mentor and even if they find one, they don't find them matching their goals and personality. This creates a problem of lack of proper guidance that people need to move further not only in career but in life as well. In most existing online mentorship systems in universities, matching mentors and mentees is a manual job. The proposed solution is to build a mentor recommendation system in which the user is connected with the appropriate mentor based on the mentee's needs. With this mentor referral system, the battle to find the perfect mentor will be over. This recommendation system uses KNN item based collaborative filtering technique instead of support vector machine method to make it more efficient. An online survey has been performed to find the difficulties faced by the students with respect to the mentors allotted to them. The usage of recommender systems, as well as trust and reputation processes, can aid in the elimination of manual matchmaking and allow for a more workable and active perspective that adjusts according to the needs of the users. Having the correct pair instead of randomly matching students would be more advantageous, not only in mentoring, but also in promoting the student in his field of interest, because the mentor can guide the student or mentee in expanding his or her horizons and go deeper in that subject and passion.