Xiangping Wu, Zheng Zhang, Wangjun Wan, Shuaiwei Yao
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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</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Personalized behavior modeling network for human mobility prediction\",\"authors\":\"Xiangping Wu, Zheng Zhang, Wangjun Wan, Shuaiwei Yao\",\"doi\":\"10.1007/s12652-024-04806-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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. 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Experimental results demonstrate that PBMN outperforms five mainstream prediction methods concerning three commonly used evaluation metrics, emphasizing the effectiveness of PBMN</p>\",\"PeriodicalId\":14959,\"journal\":{\"name\":\"Journal of Ambient Intelligence and Humanized Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Ambient Intelligence and Humanized Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s12652-024-04806-x\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ambient Intelligence and Humanized Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12652-024-04806-x","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
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
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