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Annals of Computer Science and Information Systems, Volume 18

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

Analysis of Relationship between Personal Factors and Visiting Places using Random Forest Technique

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DOI: http://dx.doi.org/10.15439/2019F318

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 725–732 ()

Full text

Abstract. There has been research regarding relationship between human personalities and visiting places using Big Five Factor (BFF). However, other factors such as Social media usage, Hobby, Gender, Age, and Religion and so on are regarded as also major factors which effects the choice of visiting place of a person. Using questionnaire designed by authors, these factors as well as BFF were prepared for this research. The visiting places were collected by a smartphone app called SWARM and classified in 10 categories. In sum, personal data of 34 participants had been collected for several months. To figure out the relationship between these factors and visiting places, random forest technique of ensemble method was used.

References

  1. H. Y. Song and E. B. Lee, “An analysis of the relationship between human personality and favored location,” AFIN 2015, p. 12, 2015.
  2. H. Y. Song and H. B. Kang, “Analysis of relationship between personality and favorite places with poisson regression analysis,” ITM Web of Conferences, vol. 16, p. 02001, 2018. http://dx.doi.org/10.1051/itmconf/20181602001. [Online]. Available: https://doi.org/10.1051%2Fitmconf%2F20181602001
  3. S. Y. Kim and H. Y. Song, “Predicting human location based on human personality,” in Lecture Notes in Computer Science. Springer International Publishing, 2014, pp. 70–81. [Online]. Available: https://doi.org/10.1007%2F978-3-319-10353-2_7
  4. S. Kim and H. Song, “Determination coefficient analysis between personality and location using regression,” in International conference on sciences, engineering and technology innovations. Bali, ICSETI, 2015, pp. 265–274.
  5. L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001. http://dx.doi.org/10.1023/A:1010933404324
  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, jun 1992. http://dx.doi.org/10.1016/0191-8869(92)90236-i. [Online]. Available: https://doi.org/10.1016%2F0191-8869%2892%2990236-i
  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/10.1016/j.sbspro.2011.10.394. [Online]. Available: https://doi.org/10.1016%2Fj.sbspro.2011.10.394
  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, no. 5, pp. 600–615, 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,” International Journal of Contemporary Hospitality Management, vol. 25, no. 7, pp. 970–993, sep 2013. http://dx.doi.org/10.1108/ijchm-10-2012-0183. [Online]. Available: https://doi.org/10.1108%2Fijchm-10-2012-0183
  10. O. P. John, S. Srivastava et al., “The big five trait taxonomy: History, measurement, and theoretical perspectives,” Handbook of personality: Theory and research, vol. 2, no. 1999, pp. 102–138, 1999.
  11. Y. Amichai-Hamburger and G. Vinitzky, “Social network use and personality,” Computers in Human Behavior, vol. 26, no. 6, pp. 1289–1295, nov 2010. http://dx.doi.org/10.1016/j.chb.2010.03.018. [Online]. Available: https://doi.org/10.1016%2Fj.chb.2010.03.018
  12. 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/10.1016/j.chb.2014.12.038
  13. Foursquare Labs, Inc., “Swarm app,” https://www.swarmapp.com/, 2019.
  14. G. Biau and E. Scornet, “A random forest guided tour,” TEST, vol. 25, no. 2, pp. 197–227, apr 2016. http://dx.doi.org/10.1007/s11749-016-0481-7. [Online]. Available: https://doi.org/10.1007%2Fs11749-016-0481-7
  15. M. R. Segal, “Machine learning benchmarks and random forest regression,” 2004. [Online]. Available: https://escholarship.org/uc/item/35x3v9t4
  16. International Standard Classification of Occupation, “ISCO,” https://www.ilo.org/.