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A Study on Method for User Gender Prediction Using Multi-Modal Smart Device Log Data
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 Title & Authors
A Study on Method for User Gender Prediction Using Multi-Modal Smart Device Log Data
Kim, Yoonjung; Choi, Yerim; Kim, Solee; Park, Kyuyon; Park, Jonghun;
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Gender information of a smart device user is essential to provide personalized services, and multi-modal data obtained from the device is useful for predicting the gender of the user. However, the method for utilizing each of the multi-modal data for gender prediction differs according to the characteristics of the data. Therefore, in this study, an ensemble method for predicting the gender of a smart device user by using three classifiers that have text, application, and acceleration data as inputs, respectively, is proposed. To alleviate privacy issues that occur when text data generated in a smart device are sent outside, a classification method which scans smart device text data only on the device and classifies the gender of the user by matching text data with predefined sets of word. An application based classifier assigns gender labels to executed applications and predicts gender of the user by comparing the label ratio. Acceleration data is used with Support Vector Machine to classify user gender. The proposed method was evaluated by using the actual smart device log data collected from an Android application. The experimental results showed that the proposed method outperformed the compared methods.
Gender Prediction;Smart Device Log Data;Ensemble Method;Statistical Learning;Multi-Modal Data;
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인구통계특성 기반 디지털 마케팅을 위한 클릭스트림 빅데이터 마이닝,박지애;조윤호;

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