A CBR Approach for Personalizing Location-aware Services

The popularization of mobile devices in the last years has led to the development of an increasing number of services. However the technical limitations of mobile devices call for services that require minimal interactions with the user and adapt their behaviors to the user’s expectations. Context-awareness has proven to facilitate personalization of services by enabling the adaptation of the service to the user’s situation. However, the adaptation is often carried out by using pre-defined rules that only apply to some contexts. In this paper, we present our approach that addresses this limitation in location-aware services by referring to the previous actions of the user. We have developed a metric to calculate the similarity between the current user’s location and the previous ones. Based on this metric, our approach provides a personalized service by determining the service behavior expected by the user in the current location. A service, the Call Profiler service, details our approach.