The Internet of Things (IoT) was born from the fusion of virtual and physical space and became the initiator of many scientific fields. Economic sustainability is the key to further development and progress. To keep up with the changes, it is necessary to adapt economic models and concepts to meet the requirements of future smart environments. Today, the need for electronic commerce (e-commerce) has become an economic priority during the transition between Industry 4.0 and Industry 5.0. Unlike mass production in Industry 4.0, customized production in Industry 5.0 should gain additional benefits in vertical management and decision-making concepts. The authors’ research is focused on e-commerce in a three-layer vertical IoT environment. The vertical IoT concept is composed of edge, fog, and cloud layers. Given the ubiquity of artificial intelligence in data processing, economic analysis, and predictions, this paper presents a few state-of-the-art machine learning (ML) algorithms facilitating the transition from a flat to a vertical e-commerce concept. The authors also propose hands-on ML algorithms for a few e-commerce types: consumer–consumer and consumer–company–consumer relationships. These algorithms are mainly composed of convolutional neural networks (CNNs), natural language understanding (NLU), sequential pattern mining (SPM), reinforcement learning (RL for agent training), algorithms for clicking on the item prediction, consumer behavior learning, etc. All presented concepts, algorithms, and models are described in detail.

中文翻译：

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物联网环境下电子商务的未来

物联网（IoT）诞生于虚拟与物理空间的融合，并成为许多科学领域的鼻祖。经济可持续性是进一步发展和进步的关键。为了跟上变化，有必要调整经济模式和理念，以满足未来智能环境的要求。如今，电子商务（e-commerce）的需求已成为工业4.0和工业5.0过渡期间的经济优先事项。与工业4.0的大规模生产不同，工业5.0的定制生产应该在垂直管理和决策理念上获得额外的好处。作者的研究重点是三层垂直物联网环境中的电子商务。垂直物联网概念由边缘层、雾层和云层组成。鉴于人工智能在数据处理、经济分析和预测中的普遍存在，本文提出了一些最先进的机器学习（ML）算法，以促进从平面电子商务概念向垂直电子商务概念的转变。作者还提出了针对几种电子商务类型的实用机器学习算法：消费者与消费者以及消费者与公司与消费者之间的关系。这些算法主要由卷积神经网络（CNN）、自然语言理解（NLU）、序列模式挖掘（SPM）、强化学习（用于代理训练的RL）、点击项目预测算法、消费者行为学习等组成。详细描述了所有提出的概念、算法和模型。