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

Abstract

Abstract Industrial recommender systems are increasingly being used in e-commerce to personalize the shopping experience for customers, leading to improved customer satisfaction and increased sales for businesses. Although e-commerce has been spread over the world, there is not an empirical study to evaluate the performance of recommender systems for business purposes using data mining techniques and big data. In this study we have focused on real global e-commerce data for different countries, industries, and scales to see the effect of recommender systems in terms of contribution rate, click through rate, conversion rate and revenues through use of big data and data analysis techniques. We used the average values of 200 different electronic commerce (e-commerce) web sites among 25 different countries in 5 different regions. This is the first study in literature which uses and analyses the empirical data in this scale from different e-commerce web sites globally. Keywords: Recommender systems, e-commerce, big data, data mining, data analysis