Digital Service Recommendation Study Based on Mobile Communication User Behavior Analysis
Pages 65 - 71
Abstract
With the transformation and development of telecom operators, they have gradually developed various digital services in addition to basic communication services. Subscribers face to a wide range of communication plans and how to choose the services with lowest cost and best enjoyment has become a real problem. In this case, we found that there is a mismatch between consumption and services obtained for subscribers. Therefore, it makes sense to analyze the reasons and come up strategies to match them for subscribers. We set service purchase behavior as a dependent variable to study which types of services subscribers need purchase; meanwhile; we study the issue of subscriber retention. Through K-means clustering and logistic regression algorithm, we found that the subscribers’ consumption behavior changes at some specific nodes where the social identity changes. In this paper, based on the data mining technology, we innovatively research user behavior and service recommendation through collaboration algorithms in the telecommunications field, and take effective measures to help subscribers to choose the most suitable services.
References
[1]
Zhao Kai, Jiang Chao-hui. Analysis of Telecom User Behavior Features Clustering Based on K-means Algorithm. Journal of Guizhou University(Natural Sciences) 32(2), 86-90 (2015).
[2]
Xie Xue-mei, Yang Dan, Xiang Tao, Research on the Continuous Consumption Intensity of Telecom Users based on Value Comparison–A Case study of Mobile Communication Users in Universities. Citizen's Weekly (9), 42-46 (2016).
[3]
Zamfiroiu A, Ciurea C.: A Model for Users’ Profile Recognition Based on Their Behavior in Online Applications. Economic Computation, Economic computation and economic cybernetics studies and research 51(2), 181-194 (2017).
[4]
Zheng F, Liu Q.: Anomalous Telecom Customer Behavior Detection and Clustering Analysis Based on ISP's Operating Data. IEEE Access (8), 42737-42738 (2020).
[5]
Liu Liu, Li Wen-yi. Service Scheduling System Based on Service Awareness and User Behavior Analysis. Mobile Communications 43(4), 43-46 (2019).
[6]
Huang Wen-bin, Wu Jia-hui, Xu Shan-chuan. Data-driven Mobile User Behavior Analysis Framework and Methods. Information Science 34(7), 14-20 (2016).
[7]
Zulima F, Belén U. Competitive behavior in the European mobile telecommunications industry: Pioneers vs. followers. Telecommunications policy 33(7), 339-347 (2009).
[8]
Resnick P. Grouplens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of ACM 1994 Conference on Computer Supported Cooperative, pp. 175-186 (1994).
[9]
Weng Xiao-lan, Wang Zhi-jian. Research process of collaborative filtering recommendation algorithm. Computer Engineering and Applications 54(1), 25-31 (2018).
[10]
Liu Hua-feng, Jing Li-ping, Yu Jian. Survey of Matrix Factorization Based Recommendation Methods by Integrating Social Information. Journal of Software 29(2), 340-362 (2018).
[11]
Bogdan W, Vladimir F. A hybrid recommender system for recommending relevant movies using an expert system. Expert Systems with Applications (158), 2020.
[12]
Meng Xiang-Wu, Chen Cheng, Zhang Yu-jie. A Survey of Mobile News Recommend Techniques and Applications. Chinese Journal of Computers 39(4), 685-703 (2016).
[13]
Hong Liang, Ren Qiu-yuan, Liang Shu-xian. A Comparative Study of Information Service Quality of E-commerce Sites'Recommender Systems -Take Taobao, Jingdong and Amazon as Examples. Library and Information Service 60(23), 97-110 (2016).
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Published In
September 2021
411 pages
ISBN:9781450390644
DOI:10.1145/3494583
Copyright © 2021 ACM.
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Association for Computing Machinery
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Published: 09 January 2022
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- Refereed limited
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ICIBE 2021
ICIBE 2021: 2021 the 7th International Conference on Industrial and Business Engineering
September 27 - 29, 2021
Macau, China
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