Logic-Scaffolding: Personalized Aspect-Instructed Recommendation Explanation Generation using LLMs
Authors: 
Behnam Rahdari, 
Hao Ding, Ziwei Fan, Yifei Ma, Zhuotong Chen, Anoop Deoras, Branislav KvetonAuthors Info & Claims
Behnam Rahdari, Hao Ding, Ziwei Fan, Yifei Ma, Zhuotong Chen, Anoop Deoras, Branislav KvetonAuthors Info & ClaimsPages 1078 - 1081
Abstract
The unique capabilities of Large Language Models (LLMs), such as the natural language text generation ability, position them as strong candidates for providing explanation for recommendations. However, despite the size of the LLM, most existing models struggle to produce zero-shot explanations reliably. To address this issue, we propose a framework called Logic-Scaffolding, that combines the ideas of aspect-based explanation and chain-of-thought prompting to generate explanations through intermediate reasoning steps. In this paper, we share our experience in building the framework and present an interactive demonstration for exploring our results.
References
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- Logic-Scaffolding: Personalized Aspect-Instructed Recommendation Explanation Generation using LLMs
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Information & Contributors
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Published In
March 2024
1246 pages
ISBN:9798400703713
DOI:10.1145/3616855
- General Chairs:
- Luz Angélica,
- Silvio Lattanzi,
- Andrés Muñoz Medina,
- Program Chairs:
- Leman Akoglu,
- Aristides Gionis,
- Sergei Vassilvitskii
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution-NoDerivatives International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
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Published: 04 March 2024
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WSDM '24: The 17th ACM International Conference on Web Search and Data Mining
March 4 - 8, 2024
Merida, Mexico
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