A deep dive into building an ecommerce website using recommender systems
Ashu Rathore, Parshotam, Shreya Shiwangi, Abhijeet Ranjan Srivastava, N.S.V. Nandan, Srishti Murti · Computational Methods in Science and Technology · 2024
The shopping scenario of the contemporary world is filled with choices, empowering consumers but risking information overload. This paper explores user-based collaborative filtering and intelligent algorithms that create personalized shopping experiences as on user data. These systems assess user activity using filters according to content and machine learning to convert user data into personalized suggestions. The study investigates how these solutions might transform the consumer journey by reducing information overload and serving as digital guides across the complicated product environment. Also, the role of user-generated content in enhancing recommendation accuracy and increasing consumer confidence is investigated. This research also focuses on the potential approach for businesses. It investigates how consumer dispersion, guided by user-driven recommendation system data, may personalize the e-commerce experience. This insight enables organizations to strengthen consumer interactions and generate opportunities for growth in the e-commerce area.