AI-Driven Product Recommendations in eCommerce: Enhancing User Engagement and Sales
Sudarshan Prasad Nagavalli, Vishal Sresth, Aakash Srivastava · International Journal of AI BigData Computational and Management Studies · 2024
AI has become the most common technology in eCommerce platforms through which it has revolutionised the behavior of customers. Among the most effective is the product recommendation system, where AI brings the difference to the general user experience using the behavioral data, interactions history, and context information. These systems engage collaborative filtering, content-based filtering, deep learning models, and reinforcement learning to understand customer patterns and preferences and churn out seamless, timely recommendations. It also benefits customer satisfaction since customers avoid decision tiredness and are presented with products that meet their wants and expectations. This paper examines the concept of recommendation systems, the components that make recommendation architectures, methodologies used in recommendation systems in the context of today’s e-commerce environment, and performance metrics used to evaluate these systems. Performing accuracy, we estimate the number of machine learning algorithms applied to real-world datasets by benchmarking them on important KPIs like CTR, conversion rates, AOV, and user retention. Furthermore, the study also explains how, through personalization driven by AI, sales revenue improves, as well as innovation from the use of dynamic content and learning. That is why such issues as scalabilities, algorithmic bias, and cold-start are also considered, and possible directions for further research are outlined. The results help to advance the knowledge about the future possibilities to use AI as the key enabler for enhancing the customers’ experience and the company’s revenues within the context of online retail environments