用于人类移动预测的个性化行为建模网络

3区 计算机科学 Q1 Computer Science
Xiangping Wu, Zheng Zhang, Wangjun Wan, Shuaiwei Yao
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引用次数: 0

摘要

预测人类的流动性对于城市规划和个性化服务至关重要。本研究要解决的问题是分析用户行为模式并预测他们的下一个目的地。由于人类移动的复杂性和多样性,有必要从不同角度研究用户行为模式,并利用不同的上下文信息来构建预测模型。遗憾的是,以往的大多数研究往往忽视了个性化偏好,无法全面了解用户的行为模式。此外,一些研究也没有有效地挖掘和利用情境信息。针对这些不足,本文介绍了一种新颖的个性化行为建模网络(PBMN)。与现有方法相比,PBMN 提供了更全面的用户行为建模,并更广泛地利用了上下文信息,从而实现了更准确的预测。它通过两个并行通道对用户行为进行建模,同时考虑到顺序模式和个性化偏好,并充分利用不同的上下文信息。最终,它通过对不同行为特征的个性化整合生成预测结果。具体来说,PBMN 采用了一对基于注意力的编码器和解码器来模拟整体行为特征。此外,它还利用三个并行递归神经网络来模拟不同上下文信息级别的近期行为特征。我们使用两个真实世界数据集对 PBMN 的性能进行了评估。实验结果表明,在三个常用的评估指标上,PBMN 优于五种主流预测方法,突出了 PBMN 的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Personalized behavior modeling network for human mobility prediction

Personalized behavior modeling network for human mobility prediction

Predicting human mobility is essential for urban planning and personalized services. The problem addressed in this study is analyzing user behavior patterns and predicting their next destination. Due to the complexity and diversity of human mobility, it’s necessary to study user behavior patterns from various angles and leverage diverse context information to construct prediction models. Unfortunately, most previous research often neglects personalized preferences and falls short in offering a comprehensive understanding of user behavior patterns. Furthermore, some studies have not effectively mined and utilized contextual information. To address these shortcomings, this paper introduces a novel Personalized Behavior Modeling Network (PBMN). Compared to existing methods, PBMN provides a more comprehensive modeling of user behavior and utilizes context information more extensively, enabling more accurate prediction. It models user behavior through two parallel channels, taking into account both sequential patterns and personalized preferences, while fully utilizing different contextual information. Ultimately, it generates prediction results by personalized integration of different behavior features. Specifically, PBMN employs a pair of attention-based encoders and decoders to model the overall behavior features. Additionally, it utilizes three parallel recurrent neural networks to model recent behavior features at different levels of context information. The performance of PBMN was evaluated using two real-world datasets. Experimental results demonstrate that PBMN outperforms five mainstream prediction methods concerning three commonly used evaluation metrics, emphasizing the effectiveness of PBMN

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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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