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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.06507 |
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| _version_ | 1866908448610844672 |
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| author | Yang, Zhen Lin, Haitao xue, Jiawei Zhang, Ziji |
| author_facet | Yang, Zhen Lin, Haitao xue, Jiawei Zhang, Ziji |
| contents | In the past year, Generative Recommendations (GRs) have undergone substantial advancements, especially in leveraging the powerful sequence modeling and reasoning capabilities of Large Language Models (LLMs) to enhance overall recommendation performance. LLM-based GRs are forming a new paradigm that is distinctly different from discriminative recommendations, showing strong potential to replace traditional recommendation systems heavily dependent on complex hand-crafted features. In this paper, we provide a comprehensive survey aimed at facilitating further research of LLM-based GRs. Initially, we outline the general preliminaries and application cases of LLM-based GRs. Subsequently, we introduce the main considerations when LLM-based GRs are applied in real industrial scenarios. Finally, we explore promising directions for LLM-based GRs. We hope that this survey contributes to the ongoing advancement of the GR domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06507 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models Yang, Zhen Lin, Haitao xue, Jiawei Zhang, Ziji Information Retrieval Artificial Intelligence In the past year, Generative Recommendations (GRs) have undergone substantial advancements, especially in leveraging the powerful sequence modeling and reasoning capabilities of Large Language Models (LLMs) to enhance overall recommendation performance. LLM-based GRs are forming a new paradigm that is distinctly different from discriminative recommendations, showing strong potential to replace traditional recommendation systems heavily dependent on complex hand-crafted features. In this paper, we provide a comprehensive survey aimed at facilitating further research of LLM-based GRs. Initially, we outline the general preliminaries and application cases of LLM-based GRs. Subsequently, we introduce the main considerations when LLM-based GRs are applied in real industrial scenarios. Finally, we explore promising directions for LLM-based GRs. We hope that this survey contributes to the ongoing advancement of the GR domain. |
| title | GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2507.06507 |