A Context-Aware Running Route Recommender Learning from User Histories Using Artificial Neural Networks

So far, several websites exist where runners can request route information. Those systems are rather complex and lack a mobile-specific design. Thus, we propose a mobile running route recommender system (RRR) which supports the user while running or while planning the running route. The gathering and modeling of the route and its context/environment is discussed in respect of computational performance. A four dimensional plugin based ranking function is established that considers location-, time-, content-, and community-specific route features which cover all data types in our database. A conceptual model shows how the runner´s physical condition could be involved by predicting the heart rate for certain routes. Therefore, Artificial Neural Networks are chosen as data mining methodology to extend the existing recommender system.