Mining shopping data with passive tags via velocity analysis

Zhao, Jumin; Wang, Like; Li, Dengao; Li, Yanxia; Yang, Bin; Zhu, Bin; Bai, Ruke

doi:10.1186/s13638-018-1033-5

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…As for the study of exploration shopping data with passive tags via speed analysis, (Zhao et al, 2018), it confirmed that it is difficult for the actual store to collect customer shopping data during the shopping process and to conduct deep data exploration operations, as opposed to online shopping. The current methods of solving this problem concern only how data is collected and analyzed, but it does not care about the large amount of account, the amount of collected data and the long delay, as it cannot provide user feedback in a timely and effective manner.…”

Section: Literature Reviewmentioning

confidence: 91%

Data Exploration in the Supermarket System to Study the Behavior of the Jordanian Consumers

Aldiabat¹,

AL-Rousan²,

Alzoubi³

2019

Journal of Computer Science

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The study aimed to identify the extent of using data exploration in the supermarket system to study the behavior of the Jordanian consumers and the extent of using data exploration in commercial shops in Jordan. The study concluded that data exploration in the supermarket system is not used to study the behavior of the Jordanian consumers regarding the disclosure of products sold in the consumer basket. The results of the study showed that there is a use of data exploration in the supermarket system to study the behavior of the Jordanian consumers regarding the disclosure of bestselling products in a specified period. The study also showed that there is a use of data exploration in the super system to study the behavior of the Jordanian consumer regarding the exploration of consumer's behavior towards the product quality. The most important recommendations of the study is the need for further studies in the use of data exploration in the supermarket system to study the behavior of the consumers, especially as most shops use a system to handle sales movements, but there is not enough exploitation of the database, which can help significantly in studying the consumer's behavior.

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Section: Literature Reviewmentioning

confidence: 91%

Data Exploration in the Supermarket System to Study the Behavior of the Jordanian Consumers

Aldiabat¹,

AL-Rousan²,

Alzoubi³

2019

Journal of Computer Science

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“…Specifically, it uses RFID technology to capture interactions of customers with products in a store (e.g., picking up products from a shelf). Similarly, Zhao et al [29] use RFID technology to identify products that are popular among customers, as well as products that are correlated with each other. Additionally, they use their collected data to find store areas that are highly frequented (i.e., hot zones), which enables retailers to optimize their store layouts.…”

Section: Related Workmentioning

confidence: 99%

Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels

Wölbitsch¹,

Hasler²,

Walk³

et al. 2020

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

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Over the course of the last decade, online retailers have demonstrated that knowledge about customer preferences and shopping patterns is an important asset for running a successful business. For example, customer preferences and shopping histories are the foundation for recommender systems that support the search for relevant products to buy online. With the increasing adoption of modern technologies, traditional retailers are able to collect similar data about customer behavior in their stores. For example, smart fitting rooms allow to track interactions of customers with products beyond the scope of a traditional retail store. In this paper we explore how customers of a large international fashion retailer buy products online and in brick-and-mortar stores, and uncover significant differences between the two domains. In particular, we find that online customers frequently focus on buying products from one specific category, whereas customers in brick-and-mortar stores often buy a more diverse range of product types. Further, we investigate products that customers take into fitting rooms, and we find that they frequently deviate from, and complement purchases. Finally, we demonstrate how our findings impact practical applications, illustrated using recommender systems, and discuss how shopping baskets from different domains can be leveraged.

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“…the winner takes all. To solve the problem, the psych package of the R language was employed to obtain the descriptive value of each index whose distribution skews to the left [18]. Then, the bcPower function was called from the car package of R language to reduce the skewness of the skewed distributions.…”

Section: Data Processing and Index Selectionmentioning

confidence: 99%

A Hierarchical Model of E-Commerce Sellers Based on Data Mining

Bai¹

2020

ISI

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This paper attempts to accurately classify e-commerce sellers based on data mining. Firstly, the original data from an e-commerce platform were preprocessed, and the classification indices were identified from five categories (products, users, traffic, sales and basic attribute). Next, the principal component analysis (PCA) and the self-organizing feature map (SOM) were fused into a hierarchical model that divides e-commerce sellers into three categories: large sellers, medium sellers and small sellers. The effectiveness of our model was verified through experiments. Finally, several operating strategies were put forward for e-commerce sellers in each category. The research results provide a good reference for the development of the e-commerce industry.

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Mining shopping data with passive tags via velocity analysis

Cited by 8 publications

References 18 publications

Data Exploration in the Supermarket System to Study the Behavior of the Jordanian Consumers

Data Exploration in the Supermarket System to Study the Behavior of the Jordanian Consumers

Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels

A Hierarchical Model of E-Commerce Sellers Based on Data Mining

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