arXiv 2411.008232024-10-29cs.LGcs.AIcs.CLcs.SI

Mobility-LLM：利用大型语言模型从人类移动数据中学习访问意图与旅行偏好

Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Models

School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）；Beijing Key Laboratory of Traffic Data Analysis and Mining（北京市交通数据分析与挖掘重点实验室）；Department of Computer Science, Aalborg University（奥尔堡大学计算机系）

Letian Gong, Yan Lin, Xinyue Zhang, Yiwen Lu, Xuedi Han, Yichen Liu, Shengnan Guo, Youfang Lin, Huaiyu Wan

备注：: Accepted by NeurIPS2024

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AI总结：

针对现有模型忽视签到序列语义的问题，提出Mobility-LLM框架，利用大型语言模型结合访问意图记忆网络和旅行偏好提示池，在四个基准数据集和三个下游任务上显著优于现有模型。

摘要：

基于位置的服务（LBS）通过签到序列积累了涵盖多种行为的大量人类移动数据。这些序列为洞察用户的意图和偏好提供了宝贵信息。然而，现有分析签到序列的模型未能考虑这些序列中所包含的语义，而这些语义密切反映了人类的访问意图和旅行偏好，导致理解不完整。受大型语言模型（LLMs）在多个领域展现出的卓越语义理解和上下文信息处理能力的启发，我们提出了Mobility-LLM，一个利用LLMs分析签到序列以完成多项任务的新颖框架。由于LLMs无法直接解读签到数据，我们重新编程这些序列，帮助LLMs全面理解人类访问意图和旅行偏好的语义。具体而言，我们引入了一个访问意图记忆网络（VIMN）来捕捉每条记录中的访问意图，并设计了一个共享的人类旅行偏好提示池（HTPP）来引导LLM理解用户的旅行偏好。这些组件增强了模型从人类移动数据中有效提取和利用语义信息的能力。在四个基准数据集和三个下游任务上的大量实验表明，我们的方法显著优于现有模型，凸显了Mobility-LLM在推进LBS背景下对人类移动数据理解方面的有效性。

英文摘要：

Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users' intentions and preferences. Yet, existing models analyzing check-in sequences fail to consider the semantics contained in these sequences, which closely reflect human visiting intentions and travel preferences, leading to an incomplete comprehension. Drawing inspiration from the exceptional semantic understanding and contextual information processing capabilities of large language models (LLMs) across various domains, we present Mobility-LLM, a novel framework that leverages LLMs to analyze check-in sequences for multiple tasks. Since LLMs cannot directly interpret check-ins, we reprogram these sequences to help LLMs comprehensively understand the semantics of human visiting intentions and travel preferences. Specifically, we introduce a visiting intention memory network (VIMN) to capture the visiting intentions at each record, along with a shared pool of human travel preference prompts (HTPP) to guide the LLM in understanding users' travel preferences. These components enhance the model's ability to extract and leverage semantic information from human mobility data effectively. Extensive experiments on four benchmark datasets and three downstream tasks demonstrate that our approach significantly outperforms existing models, underscoring the effectiveness of Mobility-LLM in advancing our understanding of human mobility data within LBS contexts.

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