An Advanced Logic to Optimize the Product Search Engines Based on Dynamic Facet Ordering Model to Support E-Commerce Industry

This study presents an advanced logic for optimizing product search engines in the e-commerce industry using a dynamic facet ordering model. Traditional static facet-based systems often fail to adapt to real-time user behavior and product diversity, leading to inefficient search experiences. The dynamic facet ordering model introduced in this research utilizes metrics such as facet specificity and dispersion to rank product attributes in real-time, allowing for faster and more accurate product discovery. In a large-scale simulation involving consumer electronics, the dynamic facet system outperformed both expert-based static systems and entropy-based models, reducing the number of clicks by an average of 40%. User satisfaction increased by 25% on average, with satisfaction scores reaching 9.3 out of 10. The dynamic system also showed a facet selection efficiency of 82%, indicating its ability to prioritize the most relevant facets for users. With a system response time averaging 240 milliseconds, this dynamic model proved effective for enhancing both user experience and system performance. These results demonstrate the viability of dynamic facet ordering as a solution for improving e-commerce search engines.