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Designing content output for weatheraware services based on domain experts can sometimes be arduous due to their limited availability and the amount and complexity of information considered in explaining their recommendations. As an initial step in our work towards generating recommendations that are acceptable and readable, our methodology involving an eye tracker attempts to simplify and capture more valuable data in early design stages. Our pilot study explored which information in weather-based recommendations seemed to be more useful to support users decision making. The results suggest that interactive content could be deployed based on the relevance of informational items and both graphical points of interest and legends could help in delivering content more efficiently.

Supporting Content Design with an Eye Tracker: The Case of Weather-based Recommendations