PriceCop–Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform

PriceCop–Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform

Автор: Mohamed Zaim Shahrel, Sofianita Mutalib, Shuzlina Abdul-Rahman

Журнал: International Journal of Information Engineering and Electronic Business @ijieeb

Статья в выпуске: 1 vol.13, 2021 года.

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In early 2020, the world was shocked by the outbreak of COVID-19. World Health Organization (WHO) urged people to stay indoors to avoid the risk of infection. Thus, more people started to shop online, significantly increasing the number of e-commerce users. After some time, users noticed that a few irresponsible online retailers misled customers by hiking product prices before and during the sale, then applying huge discounts. Unfortunately, the “discounted” prices were found to be similar or only slightly lower than standard pricing. This problem occurs because users were unable to monitor product pricing due to time restrictions. This study proposes a Web application named PriceCop to help customers’ monitor product pricing. PriceCop is a significant application because it offers price prediction features to help users analyse product pricing within the next day; thus, it can help users to plan before making purchases. The price prediction model is developed by using Linear Regression (LR) technique. LR is commonly used to determine outcomes and used as predictors. Least Squares Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) are used as a comparison to evaluate the accuracy of the LR technique. LSSVM-ABC was initially proposed for stock market price predictions. The results show the accuracy of pricing prediction using LSSVM-ABC is 84%, while it is 62% when LR is employed. ABC is integrated into SVM to optimize the solution and is responsible for the best solution in every iteration. Even though LSSVM-ABC predicts product pricing more accurately than LR, this technique is best trained using at least a year’s worth of product prices, and the data is limited for this purpose. In the future, the dataset can be collected daily and trained for accuracy.

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Price Prediction, Linear Regression, Least Squares Support Vector Machine (LSSVM), Artificial Bee Colony (ABC)

Короткий адрес: https://sciup.org/15017428

IDR: 15017428   |   DOI: 10.5815/ijieeb.2021.01.01

Список литературы PriceCop–Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform

  • Miva, “The History Of Ecommerce: How Did It All Begin?” (October 26, 2011). Available at Miva: https://www.miva.com/blog/the-history-of-ecommerce-how-did-it-all-begin/
  • O. Hinz, I. Hann, and M. Spann, “Price Discrimination in E-Commerce? An Examination of Dynamic Pricing in Name-Your-Own Price Markets.” MIS Quarterly, vol. 35, no. 1, 2011, pp. 81–98, JSTOR, 2011.
  • F. Zuiderveen Borgesius abd J. Poort, “Online Price Discrimination and EU Data Privacy Law,” Journal of Consumer Policy, vol. 40(3), pp. 347–366, 2017.
  • T. Thörner, and T. D. Andersson, “Reference Prices in an E-commerce Context” An experiment with changing consumer prices Reference prices in an E-commerce context.
  • S.B. Hwang and S. Kim, “Dynamic pricing algorithm for E-Commerce,” Advances in Systems, Computing Sciences and Software Engineering - Proceedings of SCSS 2005, pp. 149–155, 2006.
  • W. Soon, “A review of multi-product pricing models.” Appl. Math. Comput., vol. 217, pp. 8149-8165, 2011.
  • Y.C. Lin, C. H. Huang, C. C. Hsieh, Y. C. Shu and K. T. Chuang, “Monetary discount strategies for real-time promotion campaign,” 26th International World Wide Web Conference, WWW 2017, pp. 1123–1132, 2017.
  • M. Armstrong, and Y. Chen, Discount Pricing. Mpra, 35423, Paper No. 40041, posted 17. https://doi.org/10.5897/JAERD12.088
  • N. H. Shah and V. M Dixit, “Price Discount Strategies: A Review,” Revista Investigacion Operacional, vol. 26(1), pp. 19–32, 2005.
  • P. Belleflamme, “Monopoly Price Discrimination and Privacy: The Hidden Cost of Hiding,” (October 15, 2015). Available at SSRN: https://ssrn.com/abstract=2699085 or http://dx.doi.org/10.2139/ssrn.2699085
  • A. Leibbrandt, “Behavioral constraints on price discrimination: Experimental evidence on pricing and customer antagonism,” European Economic Review, vol. 121, 2020.
  • Y. Gao and X. Wu, “A Cognitive Model Of Trust In E-Commerce: Evidence From A Field Study In China”, Journal of Applied Business Research, vol. 26, no. 1, pp. 37-44 Jan. 2010.
  • P. Romero and J. Willburn, E-Commerce & Taxation. Graziadio Business Review (Information Management/Technology (IT)), vol. 2(3), 1999.
  • F.Y. Osisanwo, J.E.T. Akinsola, O. Awodele, J. O. Hinmikaiye, O. Olakanmi, J. Akinjobi, "Supervised Machine Learning Algorithms: Classification and Comparison". International Journal of Computer Trends and Technology (IJCTT), vol. 48(3), pp. 128-138, June 2017.
  • A.A. Baharudin, S. Mutalib, N.H. Abd Hamid and N.H.A. Hamid, Price Changes Analysis Using Association Rule Mining on Online Shopping Portals,” International Journal of Engineering & Technology, vol. 7 (4.31), pp. 266-271, 2018.
  • L. Ogiela, Cognitive Information Systems in Management Chapter 3 - Intelligent Computer Data Analysis Techniques, Academic Press, February 2017, pp. 1-2.
  • K. Kumari and S. Yadav, “Linear Regression Analysis Study,” Journal of the Practice of Cardiovascular Sciences, vol. 4, pp. 36–38, 2018.
  • H. Hirose, Y. Soejima, and K. Hirose, “NNRMLR: A combined method of nearest neighbor regression and multiple linear regression.” Proceedings of the 2012 IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012, 351–356. https://doi.org/10.1109/IIAI-AAI.2012.76
  • O. Hegazy, S. O. Soliman, and M. Abdul Salam. “LSSVM-ABC Algorithm for Stock Price prediction.” International Journal of Computer Trends and Technology, 7(2), 81–92. https://doi.org/10.14445/22312803/ijctt-v7p121
  • D. den Hertog and K. Postek, “Bridging the gap between predictive and prescriptive analytics - new optimization methodology needed,” (December, 21 2016). Available at Optimization Online http://www.optimization-online.org/DB_HTML/2016/12/5779.html
  • L. Wang, Y. Cheng and D. C. Liu, "New evolution algorithm based on the standard particle swarm optimization," 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), Taipei, 2011, pp. 110-114, 2011.
  • S. Kumar, V.K. Sharma and R. Kumari, “Randomized Memetic Artificial Bee Colony Algorithm,” International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), vol. 3(1), pp. 52-62, April 2014.
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