Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913811546505216 |
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| author | Wang, Chengbing Zhang, Yang Zhu, Fengbin Zhang, Jizhi Shi, Tianhao Feng, Fuli |
| author_facet | Wang, Chengbing Zhang, Yang Zhu, Fengbin Zhang, Jizhi Shi, Tianhao Feng, Fuli |
| contents | Leveraging Large Language Models (LLMs) to harness user-item interaction histories for item generation has emerged as a promising paradigm in generative recommendation. However, the limited context window of LLMs often restricts them to focusing on recent user interactions only, leading to the neglect of long-term interests involved in the longer histories. To address this challenge, we propose a novel Automatic Memory-Retrieval framework (AutoMR), which is capable of storing long-term interests in the memory and extracting relevant information from it for next-item generation within LLMs. Extensive experimental results on two real-world datasets demonstrate the effectiveness of our proposed AutoMR framework in utilizing long-term interests for generative recommendation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17593 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation Wang, Chengbing Zhang, Yang Zhu, Fengbin Zhang, Jizhi Shi, Tianhao Feng, Fuli Information Retrieval Leveraging Large Language Models (LLMs) to harness user-item interaction histories for item generation has emerged as a promising paradigm in generative recommendation. However, the limited context window of LLMs often restricts them to focusing on recent user interactions only, leading to the neglect of long-term interests involved in the longer histories. To address this challenge, we propose a novel Automatic Memory-Retrieval framework (AutoMR), which is capable of storing long-term interests in the memory and extracting relevant information from it for next-item generation within LLMs. Extensive experimental results on two real-world datasets demonstrate the effectiveness of our proposed AutoMR framework in utilizing long-term interests for generative recommendation. |
| title | Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2412.17593 |