A Survey of Reasoning for Substitution Relationships: Definitions, Methods, and Directions

TL;DR

This survey comprehensively reviews methods and directions for reasoning about substitution relationships across domains, emphasizing machine learning and NLP techniques to improve understanding and prediction of substitutes.

Contribution

It provides a methodological foundation for analyzing and predicting substitute relationships, highlighting recent advances and future research directions.

Findings

Comparison of model methodologies across domains

Analysis of defining and representing substitutes

Identification of future research directions

Abstract

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.