Point of Interest (POI) recommender systems help provide their users with a location or place that they might be interested in visiting. When combined with Location-Based Social Networks (LBSNs), POI recommender systems can be restructured to recommend POI for groups of users and not just individuals. The research focused on Group Recommender Systems (GRSs) and specifically, POI GRSs are scarce when compared to recommender systems for individuals. There are two main techniques that are used for POI GRSs, the Group Profiling methods and the Users Score Aggregation methods. Both methods have their drawbacks, as the Group Profiling methods do not recommend well for new groups and the Users Score Aggregation methods generally do not perform as well as the Group Profiling methods for established groups. In this paper, we propose new result aggregation methods that use both the Group Profiling method and the Users Score Aggregation method's results to provide the best POI group recommendations without the aforementioned drawbacks.