Abstract
We propose a context-aware guide application, which provides appropriate information selected by a machine learning algorithm according to the preference and the situation of each user. We have designed and implemented the proposed system using the off-the-shelf mobile phones with a built-in GPS module. The machine learning algorithm enables our system to select an appropriate spot based on the user’s real-time context such as preference, location, weather, time, etc. As a machine learning algorithm, we use the support vector machine (SVM) to decide the appropriate information for the users. In order to realize high generalization performance, we introduce the principal component analysis (PCA) to generate the input data for the SVM learning. By our experiments in real environments, it is shown that the proposed system works correctly and the correctness of recommendation can be improved by introducing the PCA.
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Omori, Y., Wan, J., Hasegawa, M. (2012). Design and Implementation of a Context-Aware Guide Application “Kagurazaka Explorer”. In: Watanabe, T., Jain, L.C. (eds) Innovations in Intelligent Machines – 2. Studies in Computational Intelligence, vol 376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23190-2_8
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DOI: https://doi.org/10.1007/978-3-642-23190-2_8
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