Skip to main content
SpringerLink
Log in

Application of Neural Network to Predict Purchases in Online Store

  • Conference paper
  • First Online:
Information Systems Architecture and Technology: Proceedings of 37th International Conference on Information Systems Architecture and Technology – ISAT 2016 – Part IV

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 524))

Abstract

A key ability of competitive online stores is effective prediction of customers’ purchase intentions as it makes it possible to apply personalized service strategy to convert visitors into buyers and increase sales conversion rates. Data mining and artificial intelligence techniques have proven to be successful in classification and prediction tasks in complex real-time systems, like e-commerce sites. In this paper we proposed a back-propagation neural network model aiming at predicting purchases in active user sessions in a Web store. The neural network training and evaluation was performed using a set of user sessions reconstructed from server log data. The proposed neural network was able to achieve a very high prediction accuracy of 99.6 % and recall of 87.8 %.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Suchacka, G.: Statistical analysis of buying and non-buying user sessions in a web store. In: Grzech, A., Borzemski, L., Świątek, J., Wilimowska, Z. (eds.) Information Systems Architecture and Technology—Network Architecture and Applications, pp. 163–172. Wroclaw University of Technology Press (2013)

    Google Scholar 

  2. Vidushi, D., Singh, Y.: SOM improved neural network approach for next page prediction. IJCSMC 4(6), 175–181 (2015)

    Google Scholar 

  3. Belhaj, S., Tagina, M.: Modeling and prediction of the internet end-to-end delay using recurrent neural networks. J. Netw. 4(6), 528–535 (2009)

    Google Scholar 

  4. Uma, S., Geetha, D.E., Kumar, T.V.S.: Performance assessment of web service: a neural network approach. IJCA 5(6), 57–76 (2015)

    Google Scholar 

  5. Zatwarnicki, K.: Adaptive control of cluster-based web systems using neuro-fuzzy models. AMCS 22(2), 365–377 (2012)

    MATH  Google Scholar 

  6. Zatwarnicka, A., Zatwarnicki, K.: Adaptive HTTP request distribution in time-varying environment of globally distributed cluster-based web system. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011. LNCS, vol. 6881, pp. 141–150. Springer, Heidelberg (2011)

    Google Scholar 

  7. Chou, P.-H., Li, P.-H., Chen, K.-K., Wu, M.-J.: Integrating web mining and neural network for personalized E-commerce automatic service. Expert Syst. Appl. 37(4), 2898–2910 (2010)

    Article  Google Scholar 

  8. Zhang, X., Edwards, J., Harding, J.: Personalised online sales using web usage data mining. Comput. Ind. 58(8–9), 772–782 (2007)

    Article  Google Scholar 

  9. Sohrabi, B., Mahmoudian, P., Raeesi, I.: A framework for improving E-commerce websites usability using a hybrid genetic algorithm and neural network system. Neural Comput. Appl. 21(5), 1017–1029 (2012)

    Article  Google Scholar 

  10. Graña, M., Nuñez-Gonzalez, J.D., Ozaeta, L., Kamińska-Chuchmała, A.: Experiments of trust prediction in social networks by artificial neural networks. Cybern. Syst. 46(1–2), 19–34 (2015)

    Article  Google Scholar 

  11. Doganis, P., Alexandridis, A., Patrinos, P., Sarimveis, H.: Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing. J. Food Eng. 75, 196–204 (2006)

    Article  Google Scholar 

  12. Alon, I., Qi, M., Sadowski, R.J.: Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods. JRCS 8(3), 147–156 (2001)

    Google Scholar 

  13. Hop, W.: Web-shop order prediction using machine learning. Masters Thesis, Erasmus University Rotterdam (2013)

    Google Scholar 

  14. Suchacka, G., Skolimowska-Kulig, M., Potempa, A.: A k-nearest neighbors method for classifying user sessions in E-commerce scenario. JTIT 3, 64–69 (2015)

    Google Scholar 

  15. Suchacka, G., Skolimowska-Kulig, M., Potempa, A.: Classification of E-customer sessions based on support vector machine. In: ECMS’15, pp. 594–600. ECMS (2015)

    Google Scholar 

  16. Bogatinov, D.S., Bogdanoski, M., Angelevski, S.: AI-based cyber defense for more secure cyberspace. In: Handbook of Research on Civil Society and National Security in the Era of Cyber Warfare, pp. 220–237 (2016)

    Google Scholar 

  17. Safa, N.S., Ghani, N.A., Ismail, M.A.: An artificial neural network classification approach for improving accuracy of customer identification in E-commerce. MJCS 27(3), 171–185 (2014)

    Google Scholar 

  18. Lalis, J.T., Gerardo, B.D., Byun, Y.: An adaptive stopping criterion for backpropagation learning in feedforward neural network. IJMUE 9(8), 149–156 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Mathematics and Computer Science, Opole University, ul. Oleska 48, 45-052, Opole, Poland

    Grażyna Suchacka & Sławomir Stemplewski

Authors
  1. Grażyna Suchacka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sławomir Stemplewski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Grażyna Suchacka .

Editor information

Editors and Affiliations

  1. Department of Management Systems,Faculty of Computer Science and Management, Wrocław University of Technology, Wrocła Department of Management Systems, Wrocław, Poland

    Zofia Wilimowska

  2. Department of IT and Management, Wrocław University of Technology Department of IT and Management, Wrocław, Poland

    Leszek Borzemski

  3. Department of Computer Science, Wrocław University of Technology Department of Computer Science, Wrocław, Poland

    Adam Grzech

  4. Department of Computer Science, Wrocław University of Technology Department of Computer Science, Wrocław, Poland

    Jerzy Świątek

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Suchacka, G., Stemplewski, S. (2017). Application of Neural Network to Predict Purchases in Online Store. In: Wilimowska, Z., Borzemski, L., Grzech, A., Świątek, J. (eds) Information Systems Architecture and Technology: Proceedings of 37th International Conference on Information Systems Architecture and Technology – ISAT 2016 – Part IV. Advances in Intelligent Systems and Computing, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-319-46592-0_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46592-0_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46591-3

  • Online ISBN: 978-3-319-46592-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation