A Survey of Reasoning for Substitution Relationships: Definitions, Methods, and Directions
This is an incremental survey that synthesizes existing research on substitute relationships, aiming to improve recommendation systems for practical applications.
This survey paper examines the problem of understanding and predicting substitute relationships among products across various domains, analyzing machine learning and NLP techniques to provide a methodological foundation for substitute reasoning and recommendation systems.
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.