Recommendation systems in e-commerce work by analyzing customer data to suggest products that align with user preferences. They utilize algorithms to assess purchasing history, browsing behavior, and demographic information. There are primarily two types of recommendation systems: collaborative filtering, which makes suggestions based on similar customer behavior, and content-based filtering, which recommends products based on the attributes of items previously liked by the customer. This personalized approach enhances the shopping experience by providing tailored product suggestions, thereby increasing customer engagement and sales.