Abstract
Recent sensor technologies have enabled the capture of users’ behavior data. Given the large amount of data currently available from sensor-equipped environments, it is important to attempt characterization of the sensor data for automatically modeling users in a ubiquitous and mobile computing environment. As described herein, we propose a method that predicts a customer model using features based on customers’ behavior in a shop. We capture the customers’ behavior using various sensors in the form of the time duration and the sequence between blocks in the shop. Based on behavior data from the sensors, we design features that characterize the behavior pattern of a customer in the shop. We employ those features using a machine learning approach to predict customer attributes such as age, gender, occupation, and interest. Our results show that our designed behavior-based features perform with F-values of 70–90% for prediction. We also discuss the potential applications of our method in user modeling.
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Mori, J., Matsuo, Y., Koshiba, H., Aihara, K., Takeda, H. (2009). Predicting Customer Models Using Behavior-Based Features in Shops. In: Houben, GJ., McCalla, G., Pianesi, F., Zancanaro, M. (eds) User Modeling, Adaptation, and Personalization. UMAP 2009. Lecture Notes in Computer Science, vol 5535. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02247-0_14
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DOI: https://doi.org/10.1007/978-3-642-02247-0_14
Publisher Name: Springer, Berlin, Heidelberg
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