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New Technology of Library and Information Service  2015, Vol. 31 Issue (5): 80-87    DOI: 10.11925/infotech.1003-3513.2015.05.11
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Analysis of Mobile User Behaviors with Telecommunication Data
Huang Wenbin, Xu Shanchuan, Ma Long, Wang Jun
Department of Information Management, Peking University, Beijing 100871, China
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[Objective] This paper proposes a user model to understand mobile user behaviors. [Methods] Mobile user behaviors based on communication records from a Chinese telecom, including 10 thousand mobile users in a week with 40 thousand calls and 2 million network requests with locational information are analyzed. 14 fundamental indicators from the data are adopted based on four different categories, namely consumption level, call volume, network request, and amount of movement. [Results] Four user types, regular motion with large conversation, erratically motion with high network accessing, stay-in with economization, and erratically motion with high consumption, are finally deduced in this study by using K-means clustering method. [Limitations] Because of the limitation of user number and the quantity of data, complex machine learning methods are not used to create user model. [Conclusions] The results are valuable references to improve personalized services in mobile applications.

Key wordsUser behavior analysis      Mobile user study      Cluster      Data mining     
Received: 31 October 2014      Published: 11 June 2015
:  G35  

Cite this article:

Huang Wenbin, Xu Shanchuan, Ma Long, Wang Jun. Analysis of Mobile User Behaviors with Telecommunication Data. New Technology of Library and Information Service, 2015, 31(5): 80-87.

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[1] Moe W W. Buying, Searching, or Browsing: Differentiating Between Online Shoppers Using In-store Navigational Clickstream [J]. Journal of Consumer Psychology, 2003, 13(1-2): 29-39.
[2] Zhao G, Shan Q, Xiao S, et al. Modeling Web Browsing on Mobile Internet [J]. IEEE Communications Letters, 2011, 15(10): 1081-1083.
[3] Xu K, Wang F. Behavioral Graph Analysis of Internet Applications [C]. In: Proceedings of 2011 IEEE Global Telecommunications Conference (GLOBECOM'11), Houston, USA. IEEE, 2011: 1-5.
[4] Hung C, Peng W. A Regression-based Approach for Mining User Movement Patterns from Random Sample Data [J]. Data & Knowledge Engineering, 2011, 70(1): 1-20.
[5] Yavas G, Katsaros D, Ulusoy Ö, et al. A Data Mining Approach for Location Prediction in Mobile Environments [J]. Data & Knowledge Engineering, 2005, 54(2): 121-146.
[6] Zhu Y, Zhang Y, Shang W, et al. Trajectory Enabled Service Support Platform for Mobile Users' Behavior Pattern Mining [C]. In: Proceedings of the 6th Annual International Mobile and Ubiquitous Systems: Networking & Services (MobiQuitous' 09), Toronto, Canada. IEEE, 2009: 1-10.
[7] Akoush S, Sameh A. Mobile User Movement Prediction Using Bayesian Learning for Neural Networks [C]. In: Proceedings of the 2007 International Conference on Wireless Communications and Mobile Computing (IWCMC'07). New York: ACM, 2007: 191-196.
[8] 谭钧元, 宋国杰, 谢昆青, 等. 一种有效的基于生活熵的移动用户分类算法 [J]. 计算机研究与发展, 2009, 46(Sl): 433-438. (Tan Junyuan, Song Guojie, Xie Kunqing, et al. An Effective Mining Method for Mobile Subscribers Based on Life Entropy [J]. Journal of Computer Research and Development, 2009, 46(Sl): 433-438.)
[9] Shafqat Ali Shad. 移动用户轨迹与行为模式挖掘方法研究[D]. 合肥: 中国科学技术大学, 2013. (Shafqat Ali Shad. Mobile User Trajectory and Profile Mining [D]. Hefei: University of Science and Technology of China, 2013.)
[10] 梁鹏, 李兵, 郭文君. 移动数据业务用户行为模型研究[J]. 中兴通讯技术, 2005(4): 70-74. (Liang Peng, Li Bing, Guo Wenjun. Research on User Behavior Models of Mobile Data Services [J]. ZTE Communications, 2005(4): 70-74.)
[11] 吕洋. 基于用户行为数据分析的移动互联业务推荐模型[D]. 武汉: 华中科技大学, 2011. (Lv Yang. A Mobile Internet Business Recommendation Model Based on Data Analysis of User Behavior [D]. Wuhan: Huazhong University of Science and Technology, 2011.)
[12] Liu Y, Yuan P. A Study of User Downloading Behavior in Mobile Internet Using Clickstream Data [C]. In: Proceedings of the 3rd International Symposium on Intelligent Information Technology and Security Informatics, Jinggangshan, China. IEEE, 2010: 255-257.
[13] Zhao G, Lai W, Xu C, et al. Analysis of User Behavior in Mobile Internet Using Bipartite Network [C]. In: Proceedings of the 8th International Conference on Mobile Ad-hoc and Sensor Networks, Chengdu, China. IEEE, 2012: 38-44.
[14] 赖志伟, 梁勇华. 移动互联网用户行为分析[J]. 科技风, 2014(4): 249. (Lai Zhiwei, Liang Yonghua. Analysis of Mobile Internet User Behavior [J]. Technology Wind, 2014(4): 249.)
[15] 李勇, 徐振宁, 张维明. Internet 个性化信息服务研究综述[J]. 计算机工程与应用, 2002, 38(19): 183-188. (Li Yong, Xu Zhenning, Zhang Weiming. Internet Personalized Information Service Overview [J]. Computer Engineering and Applications, 2002, 38(19): 183-188.)
[16] 王国霞, 刘贺平. 个性化推荐系统综述[J]. 计算机工程与应用, 2012, 48(7): 66-76. (Wang Guoxia, Liu Heping. Survey of Personalized Recommendation System [J]. Computer Engineering and Applications, 2012, 48(7): 66-76.)
[17] McKinney W. Python for Data Analysis: Data Wrangling with Pandas, NumPy and Ipython [M]. O'Reilly Media Inc., 2012.

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