Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation

• URL: http://arxiv.org/abs/2412.17593v1
• Date: Mon, 23 Dec 2024 14:10:09 GMT
• Title: Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation
• Authors: Chengbing Wang, Yang Zhang, Fengbin Zhu, Jizhi Zhang, Tianhao Shi, Fuli Feng,
• Abstract summary: Large Language Models (LLMs) can harness user-item interaction histories for item generation. We propose a novel Automatic Memory-Retrieval framework (AutoMR) to store long-term interests in the memory.
• Score: 31.252744207805556
• License:
• 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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