AI-POWERED PRODUCT RECOMMENDATION SYSTEM IN E-COMMERCE

Authors:

Dhanashri Bhamare1 , Dr. Mrs. Netraja Mulay2

^1,2MCA Department, P.E.S Modern College of Engineering Pune, India

ABSTRACT

E-commerce platforms thrive on personalized customer experiences, where product recommendations play a pivotal role in driving engagement, conversions, and customer satisfaction. Traditional recommendation systems often rely on static rule-based filtering or collaborative approaches that fail to adapt dynamically to evolving consumer behavior. To overcome these limitations, an AI-powered product recommendation system is proposed, leveraging machine learning, deep learning, and natural language processing to deliver real-time, personalized suggestions.

The system integrates user behavior tracking, product metadata analysis, and contextual signals to generate recommendations using hybrid models

such as collaborative filtering, content-based filtering, and deep neural networks. By employing scalable cloud infrastructure and real-time data pipelines, the solution ensures high responsiveness and adaptability across diverse e-commerce environments.

This research demonstrates how AI-driven personalization enhances customer retention, increases sales, and optimizes inventory utilization. The modular architecture allows seamless integration into existing e-commerce platforms, while explainable AI techniques ensure transparency and trust in recommendations.

Keywords: E-commerce, AI Recommendation System, Collaborative Filtering, Deep Learning, Personalization, Customer Engagement