Smart Location Recommendation for Group Meetups: A Machine Learning Perspective

Abstract-With the increasing reliance on technology for location-based services, finding an optimal and fair meetup spot for a group remains a challenge. This research proposes a machine learning-based approach to recommend the most suitable meetup location based on the geographic inputs of multiple users. The system utilizes clustering algorithms to identify equidistant locations while incorporating user preferences such as venue type (e.g., cafes, parks, malls). Google Maps API is leveraged for real-time location data and distance calculations, while various machine learning models, including K-Means and DBSCAN, are compared for efficiency and accuracy. The system enhances decision-making by offering optimized suggestions, ensuring fairness and accessibility for all participants. Future improvements include incorporating real-time traffic data and personalized recommendations based on user behavior.