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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.08687 |
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| _version_ | 1866929313163509760 |
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| author | Yang, Anxin Du, Zhijuan Sun, Tao |
| author_facet | Yang, Anxin Du, Zhijuan Sun, Tao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08687 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey of Reasoning for Substitution Relationships: Definitions, Methods, and Directions Yang, Anxin Du, Zhijuan Sun, Tao Information Retrieval Artificial Intelligence 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. |
| title | A Survey of Reasoning for Substitution Relationships: Definitions, Methods, and Directions |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2404.08687 |