Recommender Systems in E-Commerce: Personalizing Product Discovery With Data Science
International Research Journal of Modernization in Engineering Technology and Science · 2024
The rapid growth in e-commerce has reshaped the retail landscape along with the consumers interaction to the brand and products.Online shoppers have millions of choices at fingertips but paradoxically the myriad of choice may leave towards decision fatigue and frustration.In thin scenario, recommender system steps up as a viable significant tool that enables users for self-discovery over products through a less debilitating extent of choices.These systems, through the application of data science techniques, give recommendations to individual user based on their preferences and behaviors and ensure high driving sales for e-commerce businesses.This review paper explores the pivotal role of recommender systems in enhancing product discovery within ecommerce platforms.As digital marketplaces become increasingly crowded, the ability to deliver personalized recommendations has emerged as a critical factor for user engagement and satisfaction.This paper discusses the different types of recommender systems, such as collaborative filtering, content-based filtering and hybrid approaches with their benefit and limitations.Personalized recommendation in the context of user engagement is also explored.Tailored suggestions lead to increased user satisfaction, retention and sales while enhancing customer experience.The paper also analyzes the technology landscape of recommender systems, focusing on some important algorithms and tools including matrix factorization and deep learning.Finally, the paper identifies future trends including explainable recommendations, context-aware systems, and AI-driven predictive recommendations that promise to further enhance the effectiveness of the recommender systems in delivering relevant and engaging user experiences.