The advent of Artificial General Intelligence (AGI) has heralded a new era in e-commerce, empowering recommendation systems with its advanced capabilities, yet concerns about fairness in these systems have emerged. This paper presents a comprehensive study examining user gender fairness in various recommendation algorithms and domains, with a particular focus on AI-enabled and LLM-based recommendation systems. Concretely, we conduct experiments on four datasets from distinct domains to evaluate and compare the gender fairness of eleven recommendation models from six families under several fairness metrics, such as Absolute Difference, Item Coverage, and Gini coefficient. Our findings reveal significant disparities in recommendation accuracy and diversity between male and female users, highlighting the need for fair and unbiased recommendation services in e-commerce. Notably, the latest LLM-based recommendation model demonstrates promising fairness in terms of Item Coverage and Gini coefficient between male and female users, suggesting its potential in mitigating gender bias in recommendations. This study contributes to the understanding of gender fairness in different families of recommendation systems and provides insights for recommendation system design in e-commence platforms.