A K-means++ Based User Classification Method for Social E-commerce

Cui, Haoliang; Niu, Shaozhang; Li, Keyue; Shi, Chengjie; Shao, Shuai; Gao, Zhenguang

doi:10.32604/iasc.2021.016408

Cited by 11 publications

(3 citation statements)

References 15 publications

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“…E-commerce has developed rapidly around the world, and it is widely regarded as an indispensable part of the life of consumers. With the continuous development of information and communication technology, livestream shopping based on e-commerce platforms has become a new shopping trend that offers consumers a new shopping experience ( 1 , 2 ). Livestream shopping technology blends e-commerce technology, social networking technology and entertainment to allow viewers to buy products with just a few taps on their mobile devices.…”

Section: Introductionmentioning

confidence: 99%

Social Distancing, Health Concerns, and Digitally Empowered Consumption Behavior Under COVID-19: A Study on Livestream Shopping Technology

Pang

Meng

Fang

et al. 2021

Front. Public Health

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During the COVID-19 pandemic, livestream shopping has provided consumers with a way to maintain social distancing while offering an alternative to offline shopping. This study aims to understand the impact of COVID-19 and other public health crises on the behavioral intentions of consumers using livestream shopping technology. A theoretical model was designed that combines the health belief model, trust theory, and the theory of planned behavior. Empirical data were collected from 358 residents in China and then analyzed using structural equation modeling. The results showed that perceived susceptibility, perceived severity, perceived benefits, and perceived obstacles had a significant impact on consumer trust. Consumer trust in turn had a direct impact on behavioral intention and an indirect impact on behavioral intention via attitude. These research results have practical implications for livestream shopping merchants, platform decision-makers, and service designers.

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Section: Introductionmentioning

confidence: 99%

Social Distancing, Health Concerns, and Digitally Empowered Consumption Behavior Under COVID-19: A Study on Livestream Shopping Technology

Pang

Meng

Fang

et al. 2021

Front. Public Health

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show abstract

“…We introduce consumer psychology as a factor into the study and use the existing K-means++ method to classify customers. Compared with the K-means++ method of Cui et al (2021) , we added the improved RFM model to the selection of clustering variables to make the results more optimal. Li (2021a) proposed to formulate strategies for enterprises from three aspects, namely, historical purchase records of customers, usage time of the customers on the platform and customers’ reviews to online shopping platforms.…”

Section: Discussionmentioning

confidence: 99%

Research on commodity business value and customer value of e-commerce platforms: Based on consumer psychology and cognition

Zheng

Wen

et al. 2022

Front. Psychol.

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Under the background of economic globalization and COVID-19, online shopping has gradually replaced offline shopping as the main shopping mode. In this paper, consumers’ perceptions are introduced into the traditional BCG matrix to form a new BCG matrix, and according to it, the small gifts of a gift e-commerce platform are classified. We then performed a robustness test comparing the BCG matrix with K-means clustering. We found that new BCG matrix can objectively reflect the value of small gifts and provide suggestions for the e-commerce platform to make subsequent product decisions. Then we judge the customer value of the platform based on the improved RFM model and K-means++ clustering, and provide a reasonable customer value classification method for the e-commerce platform. Finally, we comprehensively consider the relationship between the commodity value and customer value, and analyze the preferences of different types of customer groups for different types of small gifts. Our research result shows that small gifts can be divided into 4 categories according to commodity value, namely “stars,” “cash cows,” “questions marks,” and “dogs.” These four categories of small gifts can be converted into each other through marketing ploys. Customers can be divided into important retention customers, key loyal customers and general development customers according to their values. Faced with different types of customers, managers can adopt different strategies to extract customer value. However, consumer psychology will affect consumer cognition, and different types of consumers prefer different types of small gifts, so the precise implementation of marketing strategies will effectively improve the profitability of the gift e-commerce platform. Compared with the traditional classification method, the commodity business value classification method proposed in this paper uses management analysis and planning methods, and introduces consumer psychological factors into the commodity and customer classification, so that the classification results are more credible. In addition, we jointly analyze the results of commodity value classification and customer value classification, and analyze in detail the preferences of different valued customer groups for different types of commodities, so as to provide directions for subsequent research on customer preference.

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“…Clustering analysis is a process of grouping a collection of data into multiple subsets consisting of different similar objects [19][20][21]. Cluster analysis is used to measure the similarity between different data and has been widely developed in mathematics, biology, meteorology, etc.…”

Section: Cluster Analysismentioning

confidence: 99%

Analysis of CLARANS Algorithm for Weather Data Based on Spark

Zhang,

Wang

2023

Computers, Materials &Amp; Continua

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With the rapid development of technology, processing the explosive growth of meteorological data on traditional standalone computing has become increasingly time-consuming, which cannot meet the demands of scientific research and business. Therefore, this paper proposes the implementation of the parallel Clustering Large Application based upon RANdomized Search (CLARANS) clustering algorithm on the Spark cloud computing platform to cluster China's climate regions using meteorological data from 1988 to 2018. The aim is to address the challenge of applying clustering algorithms to large datasets. In this paper, the morphological similarity distance is adopted as the similarity measurement standard instead of Euclidean distance, which improves clustering accuracy. Furthermore, the issue of local optima caused by an improper selection of initial clustering centers is addressed by utilizing the max-distance criterion. Compared to the k-means clustering algorithm already implemented in the Spark platform, the proposed algorithm has strong robustness, can reduce the interference of outliers in the dataset on clustering results, and has higher parallel performance than the frequently used serial algorithms, thus improving the efficiency of big data analysis. This experiment compares the clustered centroid data with the annual average meteorological data of representative cities in the five typical meteorological regions that exist in China, and the results show that the clustering results are in good agreement with the meteorological data obtained from the National Meteorological Science Data Center. This algorithm has a positive effect on the clustering analysis of massive meteorological data and deserves attention in scientific research activities.

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A K-means++ Based User Classification Method for Social E-commerce

Cited by 11 publications

References 15 publications

Social Distancing, Health Concerns, and Digitally Empowered Consumption Behavior Under COVID-19: A Study on Livestream Shopping Technology

Social Distancing, Health Concerns, and Digitally Empowered Consumption Behavior Under COVID-19: A Study on Livestream Shopping Technology

Research on commodity business value and customer value of e-commerce platforms: Based on consumer psychology and cognition

Analysis of CLARANS Algorithm for Weather Data Based on Spark

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