Learn by Selling: Equipping Large Language Models with Product Knowledge for Context-Driven Recommendations
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917737466429440 |
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| author | Anand, Sarthak Jiang, Yutong Kokaia, Giorgi |
| author_facet | Anand, Sarthak Jiang, Yutong Kokaia, Giorgi |
| contents | The rapid evolution of large language models (LLMs) has opened up new possibilities for applications such as context-driven product recommendations. However, the effectiveness of these models in this context is heavily reliant on their comprehensive understanding of the product inventory. This paper presents a novel approach to equipping LLMs with product knowledge by training them to respond contextually to synthetic search queries that include product IDs. We delve into an extensive analysis of this method, evaluating its effectiveness, outlining its benefits, and highlighting its constraints. The paper also discusses the potential improvements and future directions for this approach, providing a comprehensive understanding of the role of LLMs in product recommendations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20856 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learn by Selling: Equipping Large Language Models with Product Knowledge for Context-Driven Recommendations Anand, Sarthak Jiang, Yutong Kokaia, Giorgi Information Retrieval Artificial Intelligence The rapid evolution of large language models (LLMs) has opened up new possibilities for applications such as context-driven product recommendations. However, the effectiveness of these models in this context is heavily reliant on their comprehensive understanding of the product inventory. This paper presents a novel approach to equipping LLMs with product knowledge by training them to respond contextually to synthetic search queries that include product IDs. We delve into an extensive analysis of this method, evaluating its effectiveness, outlining its benefits, and highlighting its constraints. The paper also discusses the potential improvements and future directions for this approach, providing a comprehensive understanding of the role of LLMs in product recommendations. |
| title | Learn by Selling: Equipping Large Language Models with Product Knowledge for Context-Driven Recommendations |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2407.20856 |