Abstract
Presently, significance of deep learning techniques starts to overlook the world of E-commerce with their endless customizable online shopping experience to the users. Though huge data is streaming constantly during online commerce, data imbalance problem is still unaddressed due to insufficient analytical algorithms to handle huge datasets for smooth outliers. This leads to high congestion in the network as well as the extraordinary cost problem during online commerce. The foremost objective of this work is to resolve the classification task of imbalance data and churn rate using hybrid geometric sampling and AdaBoost based deep learning classification approach that uses diverse solution to provide a balance among prediction, accuracy, precision, specificity, sensitivity, and usability of data in E-commerce. This proposed solution helps to reduce the data imbalance problem and prediction of churn as well as non-churn customers in E-commerce web links. The experimental analysis has been carried out for the proposed algorithm in accordance with conventional techniques to check the practicability of the algorithm in real time practice.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Berger, P., & Kompan, M. (2019). User modeling for churn prediction in E-commerce. IEEE Intelligent Systems,34(2), 44–52.
Fader, P. S., & Hardie, B. G. (2005). The value of simple models in new product forecasting and customer‐base analysis. Applied Stochastic models in business and industry, 21(4–5), 461–473.
Wang, C., Han, D., Fan, W., & Liu, Q. (2019). Customer churn prediction with feature embedded convolutional neural network: An empirical study in the internet funds industry. International Journal of Computational Intelligence and Applications,18(01), 1950003.
Raj, E. D., & Babu, L. D. (2017). An enhanced trust prediction strategy for online social networks using probabilistic reputation features. Neurocomputing,219, 412–421.
Liu, D. R., Liao, H. Y., Chen, K. Y., & Chiu, Y. L. (2019). Churn prediction and social neighbour influences for different types of user groups in virtual worlds. Expert Systems, e12384.
Barbado, R., Araque, O., & Iglesias, C. A. (2019). A framework for fake review detection in online consumer electronics retailers. Information Processing & Management,56(4), 1234–1244.
Aydin, F., Zhang, M., Ananda-Rajah, M., & Haffari, G. (2019). Medical multimodal classifiers under scarce data condition. arXiv preprint arXiv:1902.08888.
Meng, L., Tan, A. H., & Wunsch, D. C., II. (2019). Online multimodal co-indexing and retrieval of social media data. In L. Meng, A. H. Tan, & D. Wunsch (Eds.), Adaptive resonance theory in social media data clustering (pp. 155–174). Cham: Springer.
Shaikh, J. R., Beniwal, R., & Iliev, G. (2019). Cryptography and optimization-driven support vector neural network to mitigate DoS attacks in E-commerce. In S. Mishra (Ed.), Applications of computing, automation and wireless systems in electrical engineering (pp. 551–561). Singapore: Springer.
Lafkihi, M., Pan, S., & Ballot, E. (2019). Freight transportation service procurement: A literature review and future research opportunities in omnichannel E-commerce. Transportation Research Part E: Logistics and Transportation Review,125, 348–365.
Nolan, J., Adams, L., Haynes, G., & Little, W. (2019). Investigating online algorithms and reinforcement learning using TUE. Computer Science and Information Technology Journal, 2(1), 1–5.
Patil, M., & Rao, M. (2019). Studying the contribution of machine learning and artificial intelligence in the interface design of E-commerce site. In S. Satapathy, V. Bhateja, & S. Das (Eds.), Smart intelligent computing and applications (pp. 197–206). Singapore: Springer.
Sun, J., Zhou, M., Ai, W., & Li, H. (2018). Dynamic prediction of relative financial distress based on imbalanced data stream: from the view of one industry. Risk Management, 1–28.
Goolsbee, A. D., & Klenow, P. J. (2018). Internet rising, prices falling: Measuring inflation in a world of E-commerce. In AEA papers and proceedings (Vol. 108, pp. 488–492).
Sivasankar, E., & Vijaya, J. (2019). A study of feature selection techniques for predicting customer retention in telecommunication sector. International Journal of Business Information Systems,31(1), 1–26.
Pei, Z., & Yan, R. (2019). Cooperative behavior and information sharing in the E-commerce age. Industrial Marketing Management,76, 12–22.
Golwalkar, Y. V. (2019). U.S. Patent Application No. 10/169,806.
Sohaib, O., Naderpour, M., Hussain, W., & Martinez, L. (2019). Cloud computing model selection for E-commerce enterprises using a new 2-tuple fuzzy linguistic decision-making method. Computers & Industrial Engineering,132, 47–58.
Leung, K. H., Choy, K. L., Siu, P. K., Ho, G. T., Lam, H. Y., & Lee, C. K. (2018). A B2C E-commerce intelligent system for re-engineering the e-order fulfilment process. Expert Systems with Applications,91, 386–401.
Dolfen, P., Einav, L., Klenow, P. J., Klopack, B., Levin, J. D., Levin, L., et al. (2019). Assessing the Gains from E-commerce (No. w25610). Cambridge: National Bureau of Economic Research.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Dhote, S., Vichoray, C., Pais, R. et al. Hybrid geometric sampling and AdaBoost based deep learning approach for data imbalance in E-commerce. Electron Commer Res 20, 259–274 (2020). https://doi.org/10.1007/s10660-019-09383-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10660-019-09383-2