Flipkart Smart Recommender: AI-Driven Personalized Shopping
B. Pal, Gnanaprakasam Thangavel, Krishnamoorthi Ramalakshmi, SasiKala Rani K · 2025
Growing rapidly with e-commerce platforms, personalized product recommendation systems are a must to improve user experience and drive up the sales. Among many other, one online marketplace that uses intelligent recommendation algorithms is Flipkart, and the suggestion item is based on interests of customers, what they have browsed, and what they have purchased in the past. The aim of this research is to develop a Flipkart Product Recommendation System based on machine learning techniques and the combination of such techniques, namely collaborative filter; content-based filter, that would recommend personalized products to the customer. Finally, the study investigates several key challenges of recommendation systems: cold start problem, data sparsity, scalability, etc. It evaluates how effective various recommendation methods like Collaborative Filtering (using patterns derived from users’ interactions) and Content Based Filtering (based on product attributes) are as well. Therefore, with the idea of combination of these approaches into a hybrid model, the proposed system aims to increase recommendation accuracy and diversity. proposed according to user behavioral patterns and history of purchases. Efficient recommendation system is important, as millions of users and card inventory result In this paper, we also tackle the effects of recommendation engines on customer engagement and conversion rate, to the overall business growth. In the research, insights into the architecture and implementation of a highly efficient, scalable, and AI driven recommendation system are provided through analysis of data collection, preprocessing, feature extraction and ranking algorithms. The findings will help further the development of e-commerce recommendation technologies for enhancing satisfaction of customers on such platforms as Flipkart.