Amazon Product Recommendation System Using Apache Spark
Kushmitha Cheedella, Shaik Fathimabi, Deepika Chinamuttevi · 2024
Personalized product recommendations have emerged as a crucial component of the user experience and commercial success in the age of e-commerce. The Product Recommendation System using Apache Spark ML libraries is a data-driven system that makes advantage of the distributed computing capabilities of Apache Spark to offer users customized suggestions. In this paper, we used the Alternating Least Squares (ALS) model and Single Value Decomposition (SVD) model to develop a product collaborative filtering approach-based recommendation system to predict the top products. Our suggested system makes a list of predictions for the highest ratings using the recommender engine. We performed the experimental analysis successfully and obtained the lowest root mean squared errors (RMSEs) for SVD and ALS of 1.098 and 0.866 to 1.247, respectively. ALS is therefore regarded as the best for making product recommendations.