Research on User Behavior Based on Higher-Order Dependency Network
In the era of the popularization of the Internet of Things (IOT), analyzing people’s daily life behavior through the data collected by devices is an important method to mine potential daily requirements. The network method is an important means to analyze the relationship between people’s daily beha...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/25/8/1120 |
| _version_ | 1797584851470123008 |
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| author | Liwei Qian Yajie Dou Chang Gong Xiangqian Xu Yuejin Tan |
| author_facet | Liwei Qian Yajie Dou Chang Gong Xiangqian Xu Yuejin Tan |
| author_sort | Liwei Qian |
| collection | DOAJ |
| description | In the era of the popularization of the Internet of Things (IOT), analyzing people’s daily life behavior through the data collected by devices is an important method to mine potential daily requirements. The network method is an important means to analyze the relationship between people’s daily behaviors, while the mainstream first-order network (FON) method ignores the high-order dependencies between daily behaviors. A higher-order dependency network (HON) can more accurately mine the requirements by considering higher-order dependencies. Firstly, our work adopts indoor daily behavior sequences obtained by video behavior detection, extracts higher-order dependency rules from behavior sequences, and rewires an HON. Secondly, an HON is used for the RandomWalk algorithm. On this basis, research on vital node identification and community detection is carried out. Finally, results on behavioral datasets show that, compared with FONs, HONs can significantly improve the accuracy of random walk, improve the identification of vital nodes, and we find that a node can belong to multiple communities. Our work improves the performance of user behavior analysis and thus benefits the mining of user requirements, which can be used to personalized recommendations and product improvements, and eventually achieve higher commercial profits. |
| first_indexed | 2024-03-10T23:58:35Z |
| format | Article |
| id | doaj.art-ad5ef6537e22433ba3faa068b1e1da56 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T23:58:35Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-ad5ef6537e22433ba3faa068b1e1da562023-11-19T00:58:51ZengMDPI AGEntropy1099-43002023-07-01258112010.3390/e25081120Research on User Behavior Based on Higher-Order Dependency NetworkLiwei Qian0Yajie Dou1Chang Gong2Xiangqian Xu3Yuejin Tan4College of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaIn the era of the popularization of the Internet of Things (IOT), analyzing people’s daily life behavior through the data collected by devices is an important method to mine potential daily requirements. The network method is an important means to analyze the relationship between people’s daily behaviors, while the mainstream first-order network (FON) method ignores the high-order dependencies between daily behaviors. A higher-order dependency network (HON) can more accurately mine the requirements by considering higher-order dependencies. Firstly, our work adopts indoor daily behavior sequences obtained by video behavior detection, extracts higher-order dependency rules from behavior sequences, and rewires an HON. Secondly, an HON is used for the RandomWalk algorithm. On this basis, research on vital node identification and community detection is carried out. Finally, results on behavioral datasets show that, compared with FONs, HONs can significantly improve the accuracy of random walk, improve the identification of vital nodes, and we find that a node can belong to multiple communities. Our work improves the performance of user behavior analysis and thus benefits the mining of user requirements, which can be used to personalized recommendations and product improvements, and eventually achieve higher commercial profits.https://www.mdpi.com/1099-4300/25/8/1120higher-order dependency networks (HONs)behavior sequence analysisrandom walkvital node identificationcommunity detection |
| spellingShingle | Liwei Qian Yajie Dou Chang Gong Xiangqian Xu Yuejin Tan Research on User Behavior Based on Higher-Order Dependency Network Entropy higher-order dependency networks (HONs) behavior sequence analysis random walk vital node identification community detection |
| title | Research on User Behavior Based on Higher-Order Dependency Network |
| title_full | Research on User Behavior Based on Higher-Order Dependency Network |
| title_fullStr | Research on User Behavior Based on Higher-Order Dependency Network |
| title_full_unstemmed | Research on User Behavior Based on Higher-Order Dependency Network |
| title_short | Research on User Behavior Based on Higher-Order Dependency Network |
| title_sort | research on user behavior based on higher order dependency network |
| topic | higher-order dependency networks (HONs) behavior sequence analysis random walk vital node identification community detection |
| url | https://www.mdpi.com/1099-4300/25/8/1120 |
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