Browse > Article
http://dx.doi.org/10.15207/JKCS.2020.11.7.001

Predicting User Profile based on user behaviors  

Sim, Myo-Seop (Graduates School of Computer & Information Technology, Korea University)
Lim, Heui-Seok (Department of Computer Science and Engineering, Korea University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.7, 2020 , pp. 1-7 More about this Journal
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.
Keywords
Mobile Data; Text Analysis; Profile; Machine Learning; Recommendation System;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 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.   DOI
2 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.
3 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
4 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.
5 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
6 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.
7 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   DOI
8 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.   DOI
9 Y. J. An, G. W. Kim, &D. H. Lee. (2018). Personalized tag recommendation system using deep learning. Journal of KIISE, 112-114.
10 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.
11 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.   DOI
12 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   DOI
13 ETSI. (2016). Mobile Edge Computing (MEC); Technical Requirements. ETSI GS MEC 002 V1.1.1.
14 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
15 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   DOI
16 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