AI Tooling for Bias-Free Product Ranking and Recommendation in Retail Platforms

Authors: Udit Agarwal

DOI: https://doi.org/10.37082/IJIRMPS.v14.i2.233107

Short DOI: https://doi.org/

Country: United States

Full-text Research PDF File:   View   |   Download

Abstract: The digital retail economy is increasingly governed by recommendation engines and product ranking algorithms that curate consumer experiences and allocate market visibility. While these systems are designed to optimize for accuracy and engagement, they frequently manifest systemic biases that distort competition, marginalize niche suppliers, and reinforce societal inequalities. This research report provides a comprehensive examination of the AI tooling and architectural frameworks required to achieve bias-free product ranking in modern retail platforms. By synthesizing contemporary scholarly research and industrial technical disclosures, the analysis categorizes the multi-dimensional nature of algorithmic bias—including popularity, position, exposure, and demographic biases—and evaluates the effectiveness of pre-processing, in-processing, and post-processing mitigation strategies. The report delves into specific case studies from leading platforms such as Amazon, eBay, and Etsy, detailing their transition from binary feedback models to multi-relevance architectures and the implementation of large language models (LLMs) as evaluative judges. Furthermore, the study explores the role of microservices-oriented search architectures and specialized open-source toolkits in facilitating algorithmic transparency and governance. The findings suggest that achieving neutrality in product ranking requires a socio-technical approach that integrates rigorous statistical de-biasing, domain-driven architecture, and continuous lifecycle monitoring to preserve market integrity and consumer trust.

Keywords: Recommender Systems, Algorithmic Fairness, Product Ranking, Popularity Bias, Position Bias, Retail AI Governance, Machine Learning Fairness Tooling, Socio-technical Systems.

Paper Id: 233107

Published On: 2026-04-20

Published In: Volume 14, Issue 2, March-April 2026

Share this