Graph and Similarity-Based Approaches to On Campus Roommate Matching

Having the right roommate could be a game changer in a student’s university life by influencing academic achievements, health, and social activities. Roommate assignments are often carried out randomly or sometimes based on arrival time which can often result in suboptimal pairings. To address this, we suggest a machine learning solution that automates roommate assignment based on similarities shared between individuals in on-campus accommodation. The model implements similaritybased roommate matching, using K-Nearest Neighbours algorithm and a hybrid graph model of Louvain and Spectral Clustering, which considers students' preferences, behaviours and traits to determine compatibility with the K-Nearest Neighbours algorithm performing better and achieving similarity scores of 0.72. The target users for which the outcomes of this study are most useful are universities, their student accommodation departments