Abstract: Techniques are described for determining recommended tourist based on the real time collection and analysis of biological information regarding users. Sensors in proximity to a user may collect neural activity data (e.g., brain wave data) and physiological data (e.g., blood pressure, heart rate, blood sugar level, etc.). The biological information may be analyzed to determine, for each user, an emotion metric indicating an emotional state of the user at various times. The emotion metrics may be correlated with location data to determine the emotion metric of the user at various sites during a trip. Tag metadata describing the location(s) may be clustered through semantic analysis to generate clusters of semantically similar tags. Emotion metric scores for the clusters may be employed to predict destination(s) where the user(s) may exhibit positive emotion metrics, and the predicted destination(s) may be presented to users in advertisements or other content.

Abstract: Techniques are described for determining recommended tourist based on the real time collection and analysis of biological information regarding users. Sensors in proximity to a user may collect neural activity data (e.g., brain wave data) and physiological data (e.g., blood pressure, heart rate, blood sugar level, etc.). The biological information may be analyzed to determine, for each user, an emotion metric indicating an emotional state of the user at various times. The emotion metrics may be correlated with location data to determine the emotion metric of the user at various sites during a trip. Tag metadata describing the location(s) may be clustered through semantic analysis to generate clusters of semantically similar tags. Emotion metric scores for the clusters may be employed to predict destination(s) where the user(s) may exhibit positive emotion metrics, and the predicted destination(s) may be presented to users in advertisements or other content.