MAVROOMIE: AN END-TO-END ARCHITECTURE FOR FINDING COMPATIBLE ROOMMATES BASED ON USER PREFERENCES
Abstract
Team Formation is widely studied in literature as a method for forming teams or groups under certain constraints. However, very few works address the aspect of collaboration while forming groups under certain constraints. Motivated by the collaborative team formation, we try to extend the problem of team formation to a general problem in the real world scenario of finding compatible roommates to share a place. There are numerous applications like “roommates.com" ,”roomiematch.com" , “Roomi” which try to find roommates based on geographical and cost factors and ignore the important human factors which can play a substantial role in finding a potential roommate or roommates. We introduce "MavRoomie", an android application for finding potential roommates by leveraging the techniques of collaborative team formation in order to provide a dedicated platform for finding suitable roommates and apartments. Given a set of users, with detailed profile information, preferences, geographical and budget constraints, our goal is to present an end-to-end system for finding a cohesive group of roommates from the perspective of both the renters and homeowner. MavRoomie allows users to give their preferences and budgets which are incorporated into our algorithms in order to provide a meaningful set of roommates. The strategy followed here is similar to the Collaborative Crowdsourcing's strategy of finding a group of workers with maximized affinity and satisfying the cost and skill constraints of a task.