Recent Advances in E-commerce Recommendation Optimization A Comprehensive Review

Abstract

E-commerce recommendation systems are critical for elevating the customer experience, making more sales, and driving loyalty. With developments in artificial intelligence (AI) and machine learning (ML), there has been the capability of addressing some of these long-known issues, such as sparse data, cold-start problems, and computational complexity in such systems. This review discusses recent advances in AI-driven recommendation techniques, including deep learning models that improve accuracy, scalability, and personalization through user-item interaction data, sentiment analysis, and advanced optimization strategies like metaheuristics and hybrid approaches. Notable advancements include fine-tuned BERT models, GNN, and contextual bandits, which effectively address traditional challenges in recommendation systems. Moreover, the integration of emerging technologies such as AI-based sentiment analysis, cloud computing, and blockchain is paving the way for future innovation in this field. However, this comes with great challenges in terms of high computational demand, algorithmic bias, and poor adaptability for smaller e-commerce platforms. There is an increasing need for ethical considerations, particularly on the issues of fairness, bias mitigation, and explainability, in the development of transparent and responsible systems. This paper emphasizes the interdisciplinary approach toward developing ethical, efficient, and adaptive recommendation systems that meet the demands of dynamic online environments and the evolving preferences of users.