IEICE TRANSACTIONS on Information
Predicting User Attitude by Using GPS Location Clustering
Summary :
In these days, recognizing a user personality is an important issue in order to support various personalized services. Besides the conventional phone usage such as call logs, SMS logs and application usages, smart phones can gather the behavior of users by polling various embedded sensors such as GPS sensors. In this paper, we focus on how to predict user attitude based on GPS log data by applying location clustering techniques and extracting features from the location clusters. Through the evaluation with one month-long GPS log data, it is observed that the location-based features, such as number of clusters and coverage of clusters, are correlated with user attitude to some extent. Especially, when SVM is used as a classifier for predicting the dichotomy of user attitudes of MBTI, over 90% F-measure is achieved.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.8 pp.1600-1603
- Publication Date
- 2015/08/01
- Publicized
- 2015/05/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDL8245
- Type of Manuscript
- LETTER
- Category
- Office Information Systems, e-Business Modeling
Authors
Rajashree S. SOKASANE
Chonnam National University
Kyungbaek KIM
Chonnam National University
Keyword
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Rajashree S. SOKASANE, Kyungbaek KIM, "Predicting User Attitude by Using GPS Location Clustering" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 8, pp. 1600-1603, August 2015, doi: 10.1587/transinf.2014EDL8245.
Abstract: In these days, recognizing a user personality is an important issue in order to support various personalized services. Besides the conventional phone usage such as call logs, SMS logs and application usages, smart phones can gather the behavior of users by polling various embedded sensors such as GPS sensors. In this paper, we focus on how to predict user attitude based on GPS log data by applying location clustering techniques and extracting features from the location clusters. Through the evaluation with one month-long GPS log data, it is observed that the location-based features, such as number of clusters and coverage of clusters, are correlated with user attitude to some extent. Especially, when SVM is used as a classifier for predicting the dichotomy of user attitudes of MBTI, over 90% F-measure is achieved.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8245/_p
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@ARTICLE{e98-d_8_1600,
author={Rajashree S. SOKASANE, Kyungbaek KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Predicting User Attitude by Using GPS Location Clustering},
year={2015},
volume={E98-D},
number={8},
pages={1600-1603},
abstract={In these days, recognizing a user personality is an important issue in order to support various personalized services. Besides the conventional phone usage such as call logs, SMS logs and application usages, smart phones can gather the behavior of users by polling various embedded sensors such as GPS sensors. In this paper, we focus on how to predict user attitude based on GPS log data by applying location clustering techniques and extracting features from the location clusters. Through the evaluation with one month-long GPS log data, it is observed that the location-based features, such as number of clusters and coverage of clusters, are correlated with user attitude to some extent. Especially, when SVM is used as a classifier for predicting the dichotomy of user attitudes of MBTI, over 90% F-measure is achieved.},
keywords={},
doi={10.1587/transinf.2014EDL8245},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Predicting User Attitude by Using GPS Location Clustering
T2 - IEICE TRANSACTIONS on Information
SP - 1600
EP - 1603
AU - Rajashree S. SOKASANE
AU - Kyungbaek KIM
PY - 2015
DO - 10.1587/transinf.2014EDL8245
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2015
AB - In these days, recognizing a user personality is an important issue in order to support various personalized services. Besides the conventional phone usage such as call logs, SMS logs and application usages, smart phones can gather the behavior of users by polling various embedded sensors such as GPS sensors. In this paper, we focus on how to predict user attitude based on GPS log data by applying location clustering techniques and extracting features from the location clusters. Through the evaluation with one month-long GPS log data, it is observed that the location-based features, such as number of clusters and coverage of clusters, are correlated with user attitude to some extent. Especially, when SVM is used as a classifier for predicting the dichotomy of user attitudes of MBTI, over 90% F-measure is achieved.
ER -
English
