Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
권호 표지
DOI QR코드

DOI QR Code

Predicting User Profile based on user behaviors

모바일 사용자 행태 기반 프로파일 예측

  • Sim, Myo-Seop (Graduates School of Computer & Information Technology, Korea University) ;
  • Lim, Heui-Seok (Department of Computer Science and Engineering, Korea University)
  • 심묘섭 (고려대학교 정보통신대학원) ;
  • 임희석 (고려대학교 정보통신대학원)
  • Received : 2020.03.30
  • Accepted : 2020.07.20
  • Published : 2020.07.28
Download PDF
⟨ Previous Next ⟩

Abstract

As the performance of mobile devices has dramatically improved, users can perform many tasks in a mobile environment. This means that the use of behavior information stored in the mobile device can tell a lot of users. For example, a user's text message and frequently used application information (behavioral information) can be utilized to create useful information, such as whether the user is interested in parenting(profile prediction). In this study, I investigate the behavior information of the user that can be collected in the mobile device and propose the item that can profile the user. And I also suggest ideas about how to utilize profiling information.

모바일 디바이스의 성능이 급격히 향상됨에 따라 사용자는 많은 작업을 모바일 환경에서 할 수 있게 되었다. 이는 모바일 디바이스에 저장된 행태 정보를 활용하면 사용자의 많은 부분을 알 수 있음을 의미한다. 예를 들어, 사용자의 문자 메시지와 자주 사용하는 어플리케이션 정보(행태 정보)를 활용하여 사용자가 육아에 관심이 있는지와 같은 유용한 정보(프로파일 예측)를 만들 수 있다. 본 연구에서는 모바일 디바이스에서 수집할 수 있는 사용자의 행태 정보를 알아보고, 이를 활용하여 사용자를 프로파일링 할 수 있는 항목을 제안한다. 그리고 프로파일링 결과를 활용하여 사용자에게 편의를 줄 수 있는 정보로 활용할 수 있는 방안도 함께 제시한다.

Keywords

References

  1. Y. H. Yoo, Y. S. Choi, H. J. Park & J. H. Lee. (2020). A Study on the Effect of Personalization-Privacy- Transparency on User Trust in the Recommender System: Base on Social Media's Videos Recommendation. Journal of Digital Contents Society, 21(1), 173-184. DOI : 10.9728/dcs.2020.21.1.173
  2. ETSI. (2016). Mobile Edge Computing (MEC); Technical Requirements. ETSI GS MEC 002 V1.1.1.
  3. Y. J. Nam, D. K. Shin & D. I. Shin. (2016). A Study on the Life Log Collection and Analysis System Using Mobile. Journal of KICS, 60, 229-230. DOI : 10.1007/978-981-10-7605-3_19
  4. Da Rosa, J. H., Barbosa, J. L. & Ribeiro, G. D. (2016). ORACON: An adaptive model for context prediction. Expert Systems with Applications, 45, 56-70. https://doi.org/10.1016/j.eswa.2015.09.016
  5. S. K. Lee, Y. H. Cho & S. H. Kim. (2010). Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations. Information Sci, 180(11), 2142-2155. DOI : 10.1016/j.ins.2010.02.004
  6. S. Y. Kim & S. B. Cho. (2013). A Context-Aware Mobile Music Recommendation System to Consider User's Music Preference. Journal of KIISE, 1047-1049. DOI: 10.1145/2393347.2393368
  7. B. I. Ahn, K. I. Jung & H. L. Choi. (2017). Mobile Context Based User Behavior Pattern Inference and Restaurant Recommendation Model. Journal of Digital Contents Society, 18(3), 535-542. https://doi.org/10.9728/dcs.2017.18.3.535
  8. J. M. Kim, H. H. Song, Y. I. Ha & M. Y. Cha. (2018). Analyzing Nonverbal Cues in User Responses for Predicting the Popularity of Online Streaming Contents. Journal of KIISE, 830-832.
  9. Y. O. Kang, N. H. Cho, J. Y. Lee, J. Y. Yoon & H. J. Lee. (2019). Comparison of Tourists Classification Methods of Geotagged Photos: Empirical Models and Machine Learning Approaches, Journal of KSGIS, 29-37. DOI : 10.7319/kogsis.2019.27.4.029
  10. Y. J. Nam, D. K. Shin & D. I. Shin. (2016). A Study on the Life Log Collection and Analysis System Using Mobile. Journal of KICS, 229-230.
  11. Diab, D. M. & Hindi K. M. (2016). Using Differential Evolution for Fine Tuning Naive Bayesian Classifiers and its Application for Text Classification. Applied Soft Computing, 28, 1-60. DOI : 10.1016/j.asoc.2016.12.043
  12. JiYeon Jung & EunJong Lee. (2007). A study of situated dynamic user profile by place and role for ubiquitous service design. KSDS Conference Proceeding, 132-133.
  13. W. Wang & I. Benbasat. (2016). Empirical Assessment of Alternative Designs for Enhancing Different Types of Trusting Beliefs in Online Recommendation Agents. Journal of Management Information Systems, 33(3), 744-775. DOI : 10.1080/07421222.2016.1243949
  14. D. J. Cho, J. Y. Park, S. B. Park, J. T. Lim, J. O. Song, J, S. Bok &J. S. Yoo. (2019). Personalized Recommendation Considering Item Confidence in E-Commerce. The Journal of the Korea Contents Association, 19(3), 171-182. https://doi.org/10.5392/JKCA.2019.19.03.171
  15. Y. J. An, G. W. Kim, &D. H. Lee. (2018). Personalized tag recommendation system using deep learning. Journal of KIISE, 112-114.
  16. J. H. Shin, J. H. Song, K. S. Bok & J. S. Yoo. (2018). Personalized Travel Destination Recommendation Scheme through Hybrid Collaborative Filtering. The Journal of the Korea Contents Association, 383-384.