ADAPTIVE NEURAL RANKING SYSTEMS FOR ENHANCED PRODUCT RECOMMENDATIONS

Abstract

E-commerce platforms continually strive to improve user engagement and increase sales conversion rates by providing highly pertinent product recommendations. This study investigates the use of deep neural networks (DNNs) in an adaptive learning-to-rank framework to enhance the efficiency of E-commerce recommendation systems. The suggested architecture adapts to changing user preferences and variations in item popularity, improving the ranking of product suggestions to enhance user satisfaction and conversion rates. The system continually refines its suggestions in real-time by analyzing user interactions, ensuring that consumers are presented with the most relevant goods. An empirical assessment has shown that this adaptive learning-to-rank technique has major advantages. It has been demonstratedthat DNNs may greatly improve suggestions' relevance and accuracy in an E-commerce environment. The results of this study offer useful insights into the implementation of adaptive learning-to-rank systems, demonstrating their ability to revolutionize E-commerce platforms by enhancing user experience and increasing conversion rates.