Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis

Abstract

Recommender systems in the industrial sector are experiencing a growing application within e-commerce platforms, focusing on tailoring customer shopping experiences. This trend has led to increased customer satisfaction and enhanced sales outcomes for businesses operating in this domain. Despite the widespread prevalence of e-commerce globally, there exists a noticeable gap in the empirical assessment of recommender system performance for business objectives, particularly in the context of utilizing data mining methodologies and big data analytics. This research aims to address this gap by scrutinizing authentic global e-commerce data that spans diverse countries, industries, and scales. The primary objective is to ascertain the impact of recommender systems, measured in terms of contribution rate, click-through rate, conversion rate, and revenue, by leveraging advanced big data analytics and data mining techniques. The study utilizes average values derived from an extensive dataset comprising 200 distinct e-commerce websites, representing a spectrum of 25 countries distributed across five different regions. Notably, this research represents a pioneering initiative in the literature as it harnesses and analyzes empirical data on such a comprehensive scale derived from various global e-commerce platforms.