Skip to main content
SpringerLink
Log in
Book cover

Asian Conference on Intelligent Information and Database Systems

ACIIDS 2017: Advanced Topics in Intelligent Information and Database Systems pp 27–36Cite as

Classification of Product Rating Using Data Mining Techniques

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 710))

Abstract

Data mining is the procedure to find patterns and necessary details from huge amount of data collected from various sources for a period of time. The target of our research is to classify the rating of individual products in an online shopping website based on price, discount, number of items left, sellers, count of likes and seller followers. The online shopping website from where we collected the data for prediction is kaymu.com.bd which is an online store in Bangladesh. The product rating that we are going to predict gives the correct rating of each product that not only depends on a single user’s rating but the overall rating considering views of every other user. This helps user to decide what product to buy and how good it actually is.

This is a preview of subscription content, 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
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Rastegari, H., Sap, M.N.M.: Data mining and e-commerce: methods, applications, and challenges. Jurnal Teknologi Maklumat 116–128 (2008)

    Google Scholar 

  2. Patel et al.: Web mining in e-commerce: pattern discovery, issues, and application. Int. J. P2P Netw. Trends Technol. 1(3), 40–45 (2011)

    Google Scholar 

  3. Crone, S.F., Soopramanien, D.: Predicting customer online shopping adoption—an evaluation of data mining and market modelling approaches. In: Data Mining Conference (DMIN), pp. 20–23 (2005)

    Google Scholar 

  4. Saraee, M., et al.: A data mining approach to analysis and prediction of movie ratings. In: Data Mining V, pp 344–352. WII Press, UK (2004)

    Google Scholar 

  5. Zhang, F.: The application of visualization technology on e-commerce data mining. In: Second International Symposium on Intelligent Information Technology Application (2008)

    Google Scholar 

  6. Zhao, W., Lin, H.: WEB data mining applications in e-commerce. In: 9th International Conference on Computer Science and Education, Canada, pp. 557–559 (2014)

    Google Scholar 

  7. Horch, A., et al.: Mining e-commerce data from e-shop websites. IEEE Trustcom (2015)

    Google Scholar 

  8. Liu, B., et al.: Mining data records in web pages. In: SIGKDD USA, 2003, pp 601–606

    Google Scholar 

  9. Dlamini, M.G., et al.: Extracting interesting patterns from e-commerce databases to ensure customer loyalty. In: Proceedings of 2015 IEEE 12th International Conf. Networking, Sensing and Control, Taiwan, pp. 382–387

    Google Scholar 

  10. Andrew, Ng.Y., Michael, J.L.: On discriminative vs. generative classifiers: a comparison of logistic regression and Naïve Bayes. http://ai.stanford.edu/~ang/papers/ (2002)

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, North South University, Plot – 15, Block – B, Bashundhara, Dhaka, 1229, Bangladesh

    Pinku Deb Nath, Sowvik Kanti Das, Fabiha Nazmi Islam, Kifayat Tahmid, Raufir Ahmed Shanto & Rashedur M. Rahman

Authors
  1. Pinku Deb Nath
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sowvik Kanti Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fabiha Nazmi Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kifayat Tahmid
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Raufir Ahmed Shanto
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rashedur M. Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rashedur M. Rahman .

Editor information

Editors and Affiliations

  1. Faculty of Computer Sci. and Management, Wroclaw Univ. of Science and Technology Faculty of Computer Sci. and Management, Wroclaw, Poland

    Dariusz Król

  2. Faculty of Computer Sci. and Management, Wroclaw Univ of Science and Technology Faculty of Computer Sci. and Management, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. School of Advanced Sci. and Technology, Japan Advanced Inst. of Sci. and Tech School of Advanced Sci. and Technology, Ishikawa, Japan

    Kiyoaki Shirai

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Nath, P.D., Das, S.K., Islam, F.N., Tahmid, K., Shanto, R.A., Rahman, R.M. (2017). Classification of Product Rating Using Data Mining Techniques. In: Król, D., Nguyen, N., Shirai, K. (eds) Advanced Topics in Intelligent Information and Database Systems. ACIIDS 2017. Studies in Computational Intelligence, vol 710. Springer, Cham. https://doi.org/10.1007/978-3-319-56660-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-56660-3_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56659-7

  • Online ISBN: 978-3-319-56660-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation