  Substitute relationships are fundamental to people's daily lives across
various domains. This study aims to comprehend and predict substitute
relationships among products in diverse fields, extensively analyzing the
application of machine learning algorithms, natural language processing, and
other technologies. By comparing model methodologies across different domains,
such as defining substitutes, representing and learning substitute
relationships, and substitute reasoning, this study offers a methodological
foundation for delving deeper into substitute relationships. Through ongoing
research and innovation, we can further refine the personalization and accuracy
of substitute recommendation systems, thus advancing the development and
application of this field.
