AI-Powered Personalized Recommendation Systems for E-Commerce Platforms

Abstract

Personalized recommendation systems have become indispensable tools for enhancing user engagement and satisfaction in e-commerce platforms. While traditional approaches like collaborative filtering (CF) and content-based filtering (CBF) have shown effectiveness, they face persistent challenges including scalability issues, cold-start problems, and difficulties in adapting to dynamic user preferences. This study proposes and evaluates an advanced hybrid recommendation system that synergistically combines CF, CBF, and Recurrent Neural Networks (RNNs) to address these limitations. Using the MovieLens 20M dataset, we implement and compare multiple recommendation approaches, including traditional methods and deep learning-based solutions. Our comprehensive evaluation demonstrates that the integrated Hybrid Recommendation System+RNN model achieves superior performance across all metrics, with an RMSE of 0.81, precision of 0.96, recall of 0.90, and significant improvements in business metrics (CTR: 6.2%, Conversion Rate: 5.1%). The results highlight the effectiveness of combining static preference modelling with sequential behaviour analysis through RNNs, offering a robust solution for real-time, personalized recommendations. This research contributes to the field by presenting a scalable and adaptive recommendation framework that effectively addresses key challenges in e-commerce personalization while maintaining computational efficiency.