Automatic Discovery and Recommendation for Telecommunication Package Using Particle Swarm Optimization

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 419)


Telecommunication package is a product produced by telecom operator to satisfy different consumer groups. Telecom operator should not only consider the users’ acceptability, but also enable to maximize profits. However, how to balance the relationship of both and designing a package are very important tasks and complicated problems. At present, design of telecom package is affected greatly by designer’s subjective experience which is blind. In this paper, a new idea of automatic discovery and recommendation for telecom package is proposed. This idea is combined user’s acceptance with operator’s profit. The package model and customer model are set up based on consumption of customers. Particle swarm optimization is used for discovering an inverse package. Meanwhile, the potential customers of the targets are selected by calculating proportion of package attribute usage. Experimental results show that the proposed method has favorable performance.


Telecom packages Particle swarm optimization algorithm Automatic discovery Recommendation 



This work was supported by National Natural Science Foundation of China under Grant No. 61573166, No. 61572230, No. 61373054, No. 61472164, No. 81301298, No.61302128. Shandong Provincial Natural Science Foundation, China, under Grant ZR2015JL025. National Key Technology Research and Development Program of the Ministry of Science and Technology under Grant 2012BAF12B07-3. Jinan Youth Science & Technology Star Project under Grant No. 2013012.


  1. 1.
    Ancheng, H.: Research on the evaluation system of A Telecom Operator, University of Electronic Science and Technology of China (2011) (in Chinese)Google Scholar
  2. 2.
    Jiang, Xin Kuang, Chen, Xu: Research on prediction model of the impact of new telecom services tariff based on the customer choice behavior. J Adv. Mater. Res. 765–767, 3249–3252 (2013)CrossRefGoogle Scholar
  3. 3.
    Amin, A., Shehzad, S., Khan, C., Ali, I., Anwar, S.: Customer churn prediction in telecommunication industry: with and without counter-example. Nature-Inspired Computation and Machine Learning. LNCS, vol. 8857, pp. 206–218. Springer, International Publishing (2014)Google Scholar
  4. 4.
    Amin, A., Shehzad, S., Khan, C., Ali, I., Anwar, S.: Churn prediction in telecommunication industry using rough set approach. In: New Trends in Computational Collective Intelligence Studies in Computational Intelligence, vol. 572, pp. 83–95 (2015)Google Scholar
  5. 5.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: International Conference on Neural Networks (Perth, Australia), Piscataway, pp. 1942–1948. IEEE Service Center, Los Alamitos (1995)Google Scholar
  6. 6.
    Sevkli, Z., Sevilgen, F.E.: A hybrid particle swarm optimization algorithm for function optimization. Applications of Evolutionary Computing. LNCS, vol. 4947, pp. 585–595. Springer, Berlin (2008)Google Scholar
  7. 7.
    Dub, M., Stefek, A.: Using PSO method for system identification. Mechatronics 2013, 143–150 (2014)Google Scholar
  8. 8.
    Liang, Xiaolei, Li, Wenfeng, Zhang, Yu., Zhou, MengChu: An adaptive particle swarm optimization method based on clustering. J Soft Comput. 19, 431–448 (2015)CrossRefGoogle Scholar
  9. 9.
    Jordehi, A.R.: Particle swarm optimization for dynamic optimization problems: a review. J. Neural Comput. Appl. 25, 1507–1516 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanChina
  2. 2.Machine Intelligence Research Labs (MIR Labs)Scientific Network for Innovation and Research ExcellenceAuburnUSA

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