Recommendation System for Online Shopping Using User Behavior Analysis

Online shopping platforms generate vast amounts of user interaction data. Leveraging this data to personalize recommendations improves user experience and sales. This research project focuses on building a recommendation system based on user behavior analysis.

Problem Statement

Generic product listings reduce engagement and fail to reflect individual preferences.

Research Objectives

The objective is to analyze browsing and purchase history to deliver personalized product recommendations.

Methodology

User behavior data is processed and modeled using collaborative filtering and content-based approaches.

Technologies Used

Machine learning libraries, databases, and web integration tools are employed.

Expected Outcomes

The system increases conversion rates, improves customer satisfaction, and enhances retention.

Conclusion

This project highlights the importance of data-driven personalization in modern e-commerce.