用多任务反思机制提升隐私保护下的地点推荐准确率
MRP-LLM: Multitask Reflective Large Language Models for Privacy-Preserving Next POI Recommendation
- 通过多任务反思提取用户细粒度偏好并构建知识库
- 融合相似用户偏好，推荐准确率在三个数据集上显著提升
- 内置隐私传输模块，有效保护敏感位置信息
大语言模型（LLM）在下一兴趣点（POI）推荐中展现出巨大潜力。然而，现有方法仅采用直接零样本提示，导致用户偏好提取不充分、协同信号注入不足，且缺乏用户隐私保护。为此，我们提出一种新型多任务反思大语言模型（MRP-LLM），旨在提升推荐效果的同时保障用户隐私。具体地，多任务反思偏好提取模块利用LLM将每位用户的细粒度偏好（类别、时间、空间）提炼为知识库（KB）；邻居偏好检索模块从KB中检索并汇总相似用户的偏好以获取协同信号；随后，通过多任务提示整合用户与相似用户偏好，生成下一POI推荐。同时，在数据收集阶段，专门设计的隐私传输模块保护敏感POI数据。在三个真实世界数据集上的大量实验表明，所提MRP-LLM在保持用户隐私的前提下，显著提升了下一POI推荐的准确性。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown promising potential for next Point-of-Interest (POI) recommendation. However, existing methods only perform direct zero-shot prompting, leading to ineffective extraction of user preferences, insufficient injection of collaborative signals, and a lack of user privacy protection. As such, we propose a novel Multitask Reflective Large Language Model for Privacy-preserving Next POI Recommendation (MRP-LLM), aiming to exploit LLMs for better next POI recommendation while preserving user privacy. Specifically, the Multitask Reflective Preference Extraction Module first utilizes LLMs to distill each user's fine-grained (i.e., categorical, temporal, and spatial) preferences into a knowledge base (KB). The Neighbor Preference Retrieval Module retrieves and summarizes the preferences of similar users from the KB to obtain collaborative signals. Subsequently, aggregating the user's preferences with those of similar users, the Multitask Next POI Recommendation Module generates the next POI recommendations via multitask prompting. Meanwhile, during data collection, a Privacy Transmission Module is specifically devised to preserve sensitive POI data. Extensive experiments on three real-world datasets demonstrate the efficacy of our proposed MRP-LLM in providing more accurate next POI recommendations with user privacy preserved.
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