최근 스마트폰 사용 형태의 도움을 받아 사용자 특성을 예측하는 것은 매우 흥미롭고 주의를 사로잡는 연주 주제이다. 현재 몇몇 연구들은 사용자의 특성을 예측하기 위해 전화 사용 기록, 문자 메시지 사용 기록, 소셜 네트워크 서비스 사용 기록 등을 이용하고 있다. 이 논문에서, 우리는 MBTI 사용자 특성과 스마트폰 사용로그 간의 관계를 평가한다. 이를 위해, 스마트폰 사용 기록에서 부터 몇몇 특징들을 추출하고 이를 Naive Bayes와 SVM등의 분류기에 적용하여 사용자의 특성을 구분하였다. 사용자 특성 분석 결과의 분석을 통해 facebook사용 기록이 외향적인 사람과 내향적인 사람을 가장 잘 구분하는 것을 알 수 있었고, SVM 분류기가 Naive Bayes보다 사용자의 특성을 잘 예측하는 것을 확인하였다.
최근 스마트폰 사용 형태의 도움을 받아 사용자 특성을 예측하는 것은 매우 흥미롭고 주의를 사로잡는 연주 주제이다. 현재 몇몇 연구들은 사용자의 특성을 예측하기 위해 전화 사용 기록, 문자 메시지 사용 기록, 소셜 네트워크 서비스 사용 기록 등을 이용하고 있다. 이 논문에서, 우리는 MBTI 사용자 특성과 스마트폰 사용로그 간의 관계를 평가한다. 이를 위해, 스마트폰 사용 기록에서 부터 몇몇 특징들을 추출하고 이를 Naive Bayes와 SVM등의 분류기에 적용하여 사용자의 특성을 구분하였다. 사용자 특성 분석 결과의 분석을 통해 facebook사용 기록이 외향적인 사람과 내향적인 사람을 가장 잘 구분하는 것을 알 수 있었고, SVM 분류기가 Naive Bayes보다 사용자의 특성을 잘 예측하는 것을 확인하였다.
Recently, predicting personality with the help of smartphone usage becomes very interesting and attention grabbing topic in the field of research. At present there are some approaches towards detecting a user's personality which uses the smartphones usage data, such as call detail records (CDRs), th...
Recently, predicting personality with the help of smartphone usage becomes very interesting and attention grabbing topic in the field of research. At present there are some approaches towards detecting a user's personality which uses the smartphones usage data, such as call detail records (CDRs), the usage of short message services (SMSs) and the usage of social networking services application. In this paper, we focus on the assessing the correlation between MBTI based user personality and the smartphone usage data. We used $Na{\ddot{i}}ve$ Bayes and SVM classifier for classifying user personalities by extracting some features from smartphone usage data. From analysis it is observed that, among all extracted features facebook usage log working as the best feature for classification of introverts and extraverts; and SVM classifier works well as compared to $Na{\ddot{i}}ve$ Bayes.
Recently, predicting personality with the help of smartphone usage becomes very interesting and attention grabbing topic in the field of research. At present there are some approaches towards detecting a user's personality which uses the smartphones usage data, such as call detail records (CDRs), the usage of short message services (SMSs) and the usage of social networking services application. In this paper, we focus on the assessing the correlation between MBTI based user personality and the smartphone usage data. We used $Na{\ddot{i}}ve$ Bayes and SVM classifier for classifying user personalities by extracting some features from smartphone usage data. From analysis it is observed that, among all extracted features facebook usage log working as the best feature for classification of introverts and extraverts; and SVM classifier works well as compared to $Na{\ddot{i}}ve$ Bayes.
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문제 정의
In this paper, we focus on understanding the relationship between MBTI user personality and the smartphone usage of a user for detection of user’s personality.
This paper presents the correlation between MBTI user personality and the smartphone usage of a user towards detecting user’s personality.
가설 설정
Assumption 2: Extraverts get energized after communicating with outside world of people but introverts feel recharged after spending time alone. By considering this assumption, we used incoming and outgoing call duration to check user’s talkative nature with outside world.
제안 방법
Extraverts usually open to and motivated by outside world of people and things; introverts are “inward-tuning”, they like to be alone. Based on this theory, we developed some assumption with MBTI user personality which is generated by using four pairs of dichotomies (mainly focused on extraversion and introversion) and the smartphone usage data.
참고문헌 (11)
Balthazard, P., R.E. Potter, and J. Warren, "Expertise, extraversion and group interaction styles as performance indicators in virtual teams: how do perceptions of its performance get formed?", SIGMIS DB, Vol.35, No.1, pp.41-64, 2004.
Chittaranjan, G., J. Blom, and D. Gatica-Perez, "Who's who with Big-Five: Analyzing and Classifying personality traits with smartphones", In Wearable Computers (ISWC), 2011 15th Annual International Symposium on, IEEE, pp.29-36, 2011.
de Montjoye, Y.A., J. Quoidbach, F. Robic, and A.S. Pentland, "Predicting personality using novel mobile phone-based metrics", In Social Computing, Behavioral-Cultural Modeling and Prediction, pp.48-55. Springer Berlin Heidelberg, 2013.
de Oliveira, R., A. Karatzoglou, P.C. Cerezo, A.A.L. de Vicuna, and N. Oliver, "Towards a psychographic user model from mobile phone usage", In CHI'11 Extended Abstracts on Human Factors in Computing Systems, pp.2191-2196, ACM, 2011.
Ester, M., H.-P. Kriegel, J. Sander, and X. Xu, "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise", In Proc. 2nd Int'l Conf. on Knowledge Discovery and Data Mining, Portland, Oregon, pp.226-231, 1996.
Lee, K.M. and C. Nass, "Designing social presence of social factors in human computer interaction", In CHI '03: Proc. of the SIGCHI conf. on Human factors in computing systems, pp.289-296, New York, NY, USA, 2003.
MacQueen, J., "Some methods of classification and analysis of multivariate observations", In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297 by J.B. Macqueen, 1967.
Myers, I.B. and Mary H. McCaulley, "Myers-Briggs Type Indicator: MBTI", Consulting Psychologists Press, 1988.
Nov, O., O. Arazy, C. Lopez, and P. Brusilovsky, "Exploring personality-targe ted UI design in online social participation systems", In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 361-370. ACM, 2013.
Sokasane, R. and K Kim, "Predicting User Attitude by Using GPS Location Clustering", IEICE Transactions on Information and Systems, Vol.E98-D, No.8, pp. 1600-1603, 2015.
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