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arXiv · 2412.17593

Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation

Chengbing Wang·Yang Zhang·Fengbin Zhu·Jizhi Zhang·Tianhao Shi·Fuli Feng
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Abstract

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.

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Chengbing Wang, Yang Zhang, Fengbin Zhu, Jizhi Zhang, Tianhao Shi, Fuli Feng. 2024-12-23. Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation. https://arxiv.org/abs/2412.17593

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