Order Feature

Order features, representing complex interactions between multiple data points or variables, are a crucial focus in machine learning and related fields, aiming to improve model accuracy and interpretability. Current research emphasizes efficient methods for incorporating high-order features, employing techniques like tensor representations, selective kernel networks, and graph neural networks to manage computational complexity while capturing intricate relationships. These advancements are driving improvements in various applications, including click-through rate prediction, natural language processing (e.g., grammar rule extraction), and image processing, by enabling more accurate and insightful models.