用记忆检索让大模型记住用户长期兴趣，生成更精准推荐
Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation
- 设计记忆检索机制，让大模型存储并调用长期用户兴趣
- 在两个真实数据集上显著提升推荐效果，尤其改善长尾物品召回
- 适合做个性化推荐系统、需要理解用户长期偏好的研究者
利用大语言模型（LLMs）挖掘用户-物品交互历史进行物品生成，已成为生成式推荐的新兴范式。然而，LLM有限的上下文窗口通常导致其仅关注近期交互，忽视了更长历史中蕴含的长期兴趣。为解决此问题，我们提出一种新型自动记忆检索框架（AutoMR），可在内存中存储长期兴趣，并在生成下一个物品时从内存中提取相关信息。在两个真实世界数据集上的大量实验表明，所提出的AutoMR框架能有效利用长期兴趣，显著提升生成式推荐性能。
原文摘要 · Abstract (English)
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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