PriceCop – Price Monitor and Prediction UsingLinear Regression and LSVM-ABC Methods forE-commerce Platform

Mohamed Zaim Shahrel · Sofianita Mutalib · Shuzlina Abdul-Rahman

40 citations0/0 files storedInternational Journal of Information Engineering and Electronic Business

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Forecasting Techniques and Applications Data Mining and Machine Learning Applications

Abstract

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.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 40 citations exist upstream.

References

2018Linear regression analysis study 2017Online Price Discrimination and EU Data Privacy Law 2011Price Discrimination in E-Commerce? An Examination of Dynamic Pricing in Name-Your-Own Price Markets1 2015Monopoly Price Discrimination and Privacy: The Hidden Cost of Hiding 2011A review of multi-product pricing models 2010A Cognitive Model Of Trust In E-Commerce: Evidence From A Field Study In China 2019Behavioral constraints on price discrimination: Experimental evidence on pricing and customer antagonism 2017Monetary Discount Strategies for Real-Time Promotion Campaign

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.