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E-commerce Review System to Detect False Reviews.

Manjur Kolhar1

  • 1Department of Computer Science, College of Arts and Science, Prince Sattam Bin Abdulaziz University, Wadi Ad Dawaser, 11990, Saudi Arabia. manjur.kolhar@gmail.com.

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Summary
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This study introduces a new e-commerce review system using cumulative sum methods to detect and remove false reviews, enhancing overall rating accuracy for consumers.

Keywords:
Cloud computingCumulative sumE-commerceFalse review ratingProduct review

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Area of Science:

  • Computer Science
  • Data Science
  • E-commerce Technology

Background:

  • E-commerce platforms heavily rely on review rating systems to influence consumer decisions.
  • Existing systems often lack cumulative rating capabilities and are vulnerable to malicious feedback, known as false reviews.
  • The integrity of e-commerce review data is crucial for consumer trust and business profitability.

Purpose of the Study:

  • To develop a robust e-commerce review system framework.
  • To effectively detect and remove malicious review ratings (false reviews).
  • To provide cumulative review ratings for enhanced product assessment.

Main Methods:

  • Implementation of a novel framework for e-commerce review analysis.
  • Utilizing the cumulative sum (CUSUM) method for anomaly detection.
  • Developing algorithms to identify and filter out false reviews.

Main Results:

  • The proposed system successfully identifies and mitigates the impact of false reviews.
  • Demonstrated ability to provide accurate cumulative review ratings.
  • Improved reliability and trustworthiness of e-commerce product ratings.

Conclusions:

  • The cumulative sum method offers an effective approach to combatting false reviews in e-commerce.
  • The developed framework enhances the integrity and utility of online review systems.
  • This contributes to more informed purchasing decisions for consumers and better business insights.