An Intelligent System for Ranking E-commerce Customer Reviews to Boost Engagement

Ertuğrul Yücel*, Feyim Toprak, Tolga Kaya

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This study introduces an innovative framework that utilizes learning algorithms to rank customer reviews on e-commerce platforms. Addressing the ambiguity and subjectivity in customer feedback, our approach highlights the use of an extensive dataset and feature engineering. A pivotal part of our methodology is the creation of an original target variable named ‘adjusted action rate’ (AAR), combined with advanced training techniques to alleviate ‘position bias’. This strategy allows us to effectively capture the nuances of user behavior and review dynamics. At the core of our framework are Learning to Rank (LTR) methods, specifically designed to tackle the unique challenges of review ranking. Our primary evaluation criterion is the Normalized Discounted Cumulative Gain (nDCG) metric, which assesses the efficiency of our LTR algorithm in predicting purchase likelihood based on user reviews. Validation through online A/B testing shows that our framework significantly improves user interaction, decisionmaking efficiency, and the overall shopping experience on e-commerce sites. The results confirm the success of our strategy in overcoming the complexities of review ranking, evidenced by notable enhancements in engagement metrics.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference
EditorsCengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A. Cagri Tolga
PublisherSpringer Science and Business Media Deutschland GmbH
Pages154-161
Number of pages8
ISBN (Print)9783031671944
DOIs
Publication statusPublished - 2024
EventInternational Conference on Intelligent and Fuzzy Systems, INFUS 2024 - Canakkale, Turkey
Duration: 16 Jul 202418 Jul 2024

Publication series

NameLecture Notes in Networks and Systems
Volume1089 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2024
Country/TerritoryTurkey
CityCanakkale
Period16/07/2418/07/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

  • Artificial Intelligence in E-Commerce
  • Intelligent Review Ranking
  • Learning Algorithms
  • NLP
  • Position Bias
  • User Engagement Optimization

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