Qian Liwei, Dou Yajie, Gong Chang, Xu Xiangqian, Tan Yuejin
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.
Entropy (Basel). 2023 Jul 26;25(8):1120. doi: 10.3390/e25081120.
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.
在物联网（IOT）普及的时代，通过设备收集的数据来分析人们的日常生活行为是挖掘潜在日常需求的重要方法。网络方法是分析人们日常行为之间关系的重要手段，而主流的一阶网络（FON）方法忽略了日常行为之间的高阶依赖关系。高阶依赖网络（HON）通过考虑高阶依赖关系能够更准确地挖掘需求。首先，我们的工作采用通过视频行为检测获得的室内日常行为序列，从行为序列中提取高阶依赖规则，并重新构建一个HON。其次，将HON用于随机游走算法。在此基础上，开展关键节点识别和社区检测的研究。最后，行为数据集的结果表明，与FON相比，HON能够显著提高随机游走的准确性，改善关键节点的识别，并且我们发现一个节点可以属于多个社区。我们的工作提高了用户行为分析的性能，从而有利于用户需求的挖掘，可用于个性化推荐和产品改进，并最终实现更高的商业利润。
