Purpose The purpose of this paper is to make the mobile e-commerce shopping more convenient and avoid information overload by a mobile e-commerce recommendation system using an improved Apriori algorithm. Design/methodology/approach Combined with the characteristics of the mobile e-commerce, an improved Apriori algorithm was proposed and applied to the recommendation system. This paper makes products that are recommended to consumers valuable by improving the data mining efficiency. Finally, a Taobao online dress shop is used as an example to prove the effectiveness of an improved Apriori algorithm in the mobile e-commerce recommendation system. Findings The results of the experimental study clearly show that the mobile e-commerce recommendation system based on an improved Apriori algorithm increases the efficiency of data mining to achieve the unity of real time and recommendation accuracy. Originality/value The improved Apriori algorithm is applied in the mobile e-commerce recommendation system solving the limitation of the visual interface in a mobile terminal and the mass data that are continuously generated. The proposed recommendation system provides greater prediction accuracy than conventional systems in data mining.

With the development and deepening of the process of global integration, global health is gaining increasing attention. An increasing number of studies have examined global health from diverse perspectives to promote the realization of global public health. The purpose of this research is to systematically and comprehensively evaluate the knowledge structure, knowledge domain, and evolution trend in the field of global health research. Based on the 14,692 document data retrieved from Web of Science Core Collection from 1996 to 2019, this article carried out a visual analysis of global health research from the perspective of scientific output characteristics, scientific research cooperation networks, keywords, and highly cited literature. The results show that scholars’ interest in global health research is increasing, especially after the outbreak of SARS. USA, England, Canada, Australia, and China have the most prominent contributions to global health research. Significant authors, high impact journals and core institutions also identified. The study found that “global health governance”, “global health diplomacy”, “medical education”, “global health education” and “antimicrobial resistance” are the research frontiers and hot spots. This study provides an overview and valuable guidance for researchers and related personnel to find the research direction and practice of global health.

With rapid economic development and urbanization, a large number of primary resources are consumed and accumulate in society as recyclable resource, which causes great pressure on the environment. The development of the resource recycling industry (RRI) can reduce environmental impacts and achieve sustainable development and green growth. Scholars are paying more attention to the resource recycling industry (RRI), and the related literature continues to increase. There are over 7041 publications covering RRI in the Web of Science database from 1996 to 2018. This paper analyzes the time distribution characteristics of the literature and the status of the scientific research cooperation network using the visualization analysis software CiteSpace. The number of documents increased from 94 in 1996 to a peak of 963 in 2018. There is no relatively stable core author group. The number of papers published by “Chinese Acad Sci” ranks first among all research institutions. Document co-citation analysis and burst detection are adopted to assess the status and emerging trends in the RRI research domain. A publication by M.C. Monte on waste management is the most cited paper. Additionally, “green and sustainable and technology” and “science and technology—other topics” are the latest emerging subject categories in RRI research. Furthermore, “e-waste”, “reverse logistics” and “lean manufacturing” are emerging research trends for RRI, and “carbon emissions”, “policy”, “demolition waste”, “supply chain management” and “compressive strength” have become hot topics. These findings may provide inspiration for scholars to search for new research directions and ideas.

Environmental pollution caused by lead toxicity causes harm to human health. Lead pollution in the environment mainly comes from the processes of mining, processing, production, use, and recovery of lead. China is the world’s largest producer and consumer of refined lead. In this paper, the material flow analysis method is used to analyze the flow and direction of lead loss in four stages of lead production, manufacturing, use, and waste management in China from 1949 to 2017. The proportion coefficient of lead compounds in each stage of lead loss was determined. The categories and quantities of lead compounds discharged in each stage were calculated. The results show that in 2017, China emitted 2.1519 million tons of lead compounds. In the four stages of production, manufacturing, use, and waste management, 137.9 kilo tons, 209 kilo tons, 275 kilo tons, and 1.53 million tons were respectively discharged. The emissions in the production stage are PbS, PbO, PbSO4, PbO2, Pb2O3, and more. The emissions during the manufacturing phase are Pb, PbO, PbSO4, Pb2O3, Pb3O4, and more. The main emissions are Pb, PbO, Pb2O3, Pb3O4, and more. The main emissions in the waste management stage are PbS, Pb, PbO, PbSO4, PbO2, PbCO3, Pb2O3, Pb3O4, and more. Among them, the emissions of PbSO4, PbO, Pb, and PbO2 account for about 90%, which are the main environmental pollution emissions. The waste management stage is an important control source of lead compound emission and pollution. In view of these characteristics of the environmental pollution risk of lead compounds in China, the government should issue more targeted policies to control lead pollution.

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Abstract:With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm model. The proposed model first uses various recommendation algorithms to get a list of original recommendation results. Combined with the customer's feedback in an interactive manner, it then establishes the weights of corresponding recommendation algorithms. Finally, the synthetic formula of evidence theory is used to fuse the original results to obtain the final recommendation products. The recommendation performance of the proposed method is compared with that of traditional methods. The results of the experimental study through a Taobao online dress shop clearly show that the proposed method increases the efficiency of data mining in the consumer coverage, the consumer discovery accuracy and the recommendation recall. The hybrid recommendation algorithm complements the advantages of the existing recommendation algorithms in data mining. The interactive assigned-weight method meets consumer demand better and solves the problem of information overload. Meanwhile, our study offers important implications for e-commerce platform providers regarding the design of product recommendation systems.

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