A Model for Using Physiological Conditions for Proactive Tourist Recommendations
This addresses the need for context-aware recommendations in tourism, but it is incremental as it builds on existing wearable and recommender system technologies.
The paper tackles the problem of providing personalized tourist recommendations by proposing a data model that uses wearable sensor data (e.g., heart rate, body temperature) to infer a user's physiological condition and recommend suitable activities, demonstrating feasibility with a self-quantification app.
Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to herself and the environment. In this paper, we propose to utilize wearable sensors to gather health information about a tourist and use them for recommending tourist activities. We discuss a range of wearable devices, sensors to infer physiological conditions of the users, and exemplify the feasibility using a popular self-quantification mobile app. Our main contribution then comprises a data model to derive relations between the parameters measured by the wearable sensors, such as heart rate, body temperature, blood pressure, and use them to infer the physiological condition of a user. This model can then be used to derive classes of tourist activities that determine which items should be recommended.