Machine Learning-based Fair Product Exposure and Recommendation System for Online Marketplaces
Abstract
Online marketplaces such as Amazon, Flipkart, and eBay have transformed digital commerce by connecting buyers and sellers globally. However, one major challenge faced in these platforms is the lack of fair exposure among sellers. Popular sellers with high sales history dominate search results, while new or small-scale sellers struggle to gain visibility. This imbalance reduces competition and affects marketplace diversity. This project presents a Fair Market Exposure System (FairCart) for Online Marketplaces that aims to provide equal visibility to all sellers using a data-driven ranking approach. The system analyzes multiple factors such as seller rating, product quality, pricing, and customer reviews instead of relying only on popularity metrics. A fairness-based scoring model is developed to reduce bias and ensure balanced product ranking. The proposed system integrates a ranking algorithm with a dynamic exposure mechanism that adjusts product visibility in real time. It ensures that new sellers are given opportunities while maintaining relevance for users. A web-based dashboard is also developed to monitor seller performance, exposure levels, and ranking decisions. Experimental analysis shows that the system improves fairness in product visibility while maintaining user satisfaction. This solution provides a scalable and practical approach for enhancing transparency and fairness in online marketplaces.
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