Group Recommendation Using Psychometric Analysis

A group's success is frequently determined by the cohesion and alignment of its members’ behaviors. Thus, we have proposed an ML model that recommends group members to the user based on his/her behavioral patterns. A questionnaire based on psychometric analysis was used to collect the behavioral pattern data of second-year undergraduate engineering students. The recommendation was based the parameters such as leadership (Hierarchical and Systemic), teamwork, gender inclusivity, and social desirability. Only the top ten individuals belonging to the cluster the user was placed in were recommended. K-Means, DBSCAN, Hierarchical, and Model-Based clustering algorithms were used out of which K-Means proved to be the most efficient algorithm based on their respective silhouette scores.