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Tytuł artykułu

Analysis of the correlation between personal factors and visiting locations with boosting technique

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
Języki publikacji
EN
Abstrakty
EN
The paper analyzed the relationship between the person's fourteen characteristic factors and place to visit. The personal factors consist of personality, marital Status, final education, majors, religion, monthly income, commuting means and time, number of travel, use of SNS, time for SNS per day, life of culture. In addition, the analysis was done on which factors have the greatest impact. The analysis involved thirty-four participants and the boosting technique was used as a method of analysis. Personality data was obtained through the Big Five Factors (BFF), data for the rest of the factors were obtained through a self-created questionnaire. Location data was obtained through a Swarm application. For each location categories, the most effective factors were identified in this research.
Rocznik
Tom
Strony
743--746
Opis fizyczny
Bibliogr. 15 poz., tab., wykr.
Twórcy
autor
  • Department of Computer Engineering, Hongik Unversity, Seoul, Republic of Korea
autor
  • Department of Computer Engineering, Hongik Unversity, Seoul, Republic of Korea
Bibliografia
  • 1. S. Y. Kim and H. Y. Song, “Predicting human location based on human personality,” International Conference on Next Generation Wired/Wireless Networking, 2014. http://dx.doi.org/https://doi.org/10.1007/978-3-319-10353-2-7
  • 2. M. J. Chorley, R. M. Whitaker, and S. M. Allen, “Personality and location-based social networks,” Computers in Human Behavior, vol. 46, pp. 45 – 56, 2015. http://dx.doi.org/https://doi.org/10.1016/j.chb.2014.12.038. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0747563214007559
  • 3. F. Schapire, Robert E, Boosting: Foundations and Algorithms. MIT Press (MA), 2014.
  • 4. T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” CoRR, vol. abs/1603.02754, 2016. http://dx.doi.org/10.1145/2939672.2939785. [Online]. Available: http://arxiv.org/abs/1603.02754
  • 5. “Xgboost,” https://xgboost.readthedocs.io/en/latest/index.html, accessed: 2019-01-15.
  • 6. P. T. Costa and R. R. McCrae, “Four ways five factors are basic,” Personality and Individual Differences, vol. 13, no. 6, pp. 653 – 665, 1992. http://dx.doi.org/https://doi.org/10.1016/0191-8869(92)90236-I. [Online]. Available: http://www.sciencedirect.com/science/article/pii/019188699290236I
  • 7. J. Hoseinifar, M. M. Siedkalan, S. R. Zirak, M. Nowrozi, A. Shaker, E. Meamar, and E. Ghaderi, “An investigation of the relation between creativity and five factors of personality in students,” Procedia - Social and Behavioral Sciences, vol. 30, pp. 2037 – 2041, 2011. http://dx.doi.org/https://doi.org/10.1016/j.sbspro.2011.10.394 2nd World Conference on Psychology, Counselling and Guidance - 2011. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1877042811022191
  • 8. D. Jani, J.-H. Jang, and Y.-H. Hwang, “Big five factors of personality and tourists’ internet search behavior,” Asia Pacific Journal of Tourism Research, vol. 19, 05 2014. http://dx.doi.org/10.1080/10941665.2013.773922
  • 9. D. Jani and H. Han, “Personality, social comparison, consumption emotions, satisfaction, and behavioral intentions: How do these and other factors relate in a hotel setting?” International Journal of Contemporary Hospitality Management, vol. 25, no. 7, pp. 970–993, 2013. http://dx.doi.org/10.1108/IJCHM-10-2012-0183. [Online]. Available: https://doi.org/10.1108/IJCHM-10-2012-0183
  • 10. O. P. John and S. Srivastava, “The big-five trait taxonomy: History, measurement, and theoretical perspectives,” 1999.
  • 11. L. R. Goldberg, “’the structure of phenotypic personality traits": Author’s reactions to the six comments.” American Psychologist, vol. 48, pp. 1303–1304, 12 1993. http://dx.doi.org/10.1037/0003-066X.48.12.1303
  • 12. Y. Amichai-Hamburger and G. Vinitzky, “Social network use and personality,” Computers in Human Behavior, vol. 26, no. 6, pp. 1289–1295, 2010. http://dx.doi.org/https://doi.org/10.1016/j.chb.2010.03.018 Online Interactivity: Role of Technology in Behavior Change. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0747563210000580
  • 13. “International standard classification of occupation (isco),” https://www.ilo.org/, accessed: 2018-12-20.
  • 14. E. B. Lee and H. Y. Song, “An analysis of the relationship between human personality and favored location,” The Seventh International Conference on Advances in Future Internet, 2015.
  • 15. Song, Ha Yoon and Kang, Hwa Baek, “Analysis of relationship between personality and favorite places with poisson regression analysis,” ITM Web Conf., vol. 16, p. 02001, 2018. http://dx.doi.org/10.1051/itmconf/20181602001. [Online]. Available: https://doi.org/10.1051/itmconf/20181602001
Uwagi
1. Track 4: Information Systems and Technology
2. Technical Session: 25th Conference on Knowledge Acquisition and Management
3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d75fd46e-2d67-43dc-90b7-d1e0a478872e
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