Complementary Item

Complementary item recommendation aims to suggest products that, while different from a user's initial choice, enhance its utility or appeal. Current research focuses on improving recommendation accuracy through various techniques, including dual embedding models that capture both item similarity and complementarity, and adversarial learning approaches that leverage both user interaction data and item-specific information to handle situations with limited data. These advancements are significant for e-commerce platforms, enabling more effective product suggestions and potentially leading to increased sales and improved customer satisfaction. Furthermore, research is exploring how to address challenges like inconsistent image illumination in visual-based recommendation systems.