Shein uses an AI-driven recommendation engine to personalize the product feed and shopping experience for each user on its app and website, tailoring suggestions based on browsing history, purchase behavior, and stated preferences.

Shein's proprietary recommendation system collects and analyzes each user's browsing activity, items viewed, cart additions, and purchase history to surface tailored product suggestions, promotions, and search results. The system also feeds internal production priorities, adjusting how prominently new products are displayed based on what is trending for different user segments. This is sometimes called the large-scale automated test and reorder (LATR) model, which uses machine learning to monitor sales velocity and social engagement to determine which items should be expanded or discontinued.