Demand effects of product similarity network in e-commerce platform

Huang, Hong; Yang, Jun; Zheng, Benrong

doi:10.1007/s10660-019-09352-9

Cited by 11 publications

(17 citation statements)

References 65 publications

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“…The clustering of users and recommendations using the dynamic graph provided better results than other contemporary algorithms. Huang et al [15] incorporated a graph model to assess the similarity among products. The generated graph model was utilised to analyse the impact of network parameters on the sale of products.…”

Section: Schemaless Data Models In E-commercementioning

confidence: 99%

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A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce

Tuteja

Kumar

2021

Data Sci. Eng.

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The incorporation of heterogeneous data models into large-scale e-commerce applications incurs various complexities and overheads, such as redundancy of data, maintenance of different data models, and communication among different models for query processing. Graphs have emerged as data modelling techniques for large-scale applications with heterogeneous, schemaless, and relationship-centric data. Models exist for mapping different types of data to a graph; however, the unification of data from heterogeneous source models into a graph model has not received much attention. To address this, we propose a new framework in this study. The proposed framework first transforms data from various source models into graph models individually and then unifies them into a single graph. To justify the applicability of the proposed framework in e-commerce applications, we analyse and compare query performance, scalability, and database size of the unified graph with heterogeneous source data models for a predefined set of queries. We also access some qualitative measures, such as flexibility, completeness, consistency, and maturity for the proposed unified graph. Based on the experimental results, the unified graph outperforms heterogeneous source models for query performance and scalability; however, it falls behind for database size.

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Section: Schemaless Data Models In E-commercementioning

confidence: 99%

“…end for 19: end for to e j .properties (lines [15][16][17]. The same process is repeated for all schema graphs in the set.…”

Section: Schema and Data Unificationmentioning

confidence: 99%

A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce

Tuteja

Kumar

2021

Data Sci. Eng.

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“…Product awareness refers to the ability of consumers to recall or recognise a product, or simply whether or not consumers know about a product (Zhu et al, 2019). So far, in the field of e-commerce, studies related to awareness mainly focus on consumer (Huang, Yang, & Zheng, 2019;Barroso & Llobet, 2012;Lee, Suh, & Whang, 2003), brand (Wang & Chen, 2019;Barreda, Bilgihan, Nusair, & Okumus, 2015;Lu, Chang, & Chang, 2014), and e-commerce (Papazafeiropoulou, Pouloudi, & Doukidis, 2002). As far as product awareness is concerned, Farshcian (2001) analyzed the importance of this factor and introduced a product awareness model and described its design and implementation.…”

Section: Role Of Product Information In E-commercementioning

confidence: 99%

A Cross-Continent Analysis of the Invariance of Product Information in Cross-Border Electronic Commerce

Zhu

Mou

Cohen

2021

Journal of Global Information Management

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Cross-border electronic-commerce (CBEC) is growing. However, due to differences in culture, habits, history and language among other factors, consumers in different regions may have different perception towards the same product information on CBEC platforms, which may lead to differences in their cognition of the product with implications for purchase intentions. Presently, little research has attempted to understand whether there are such differences between global consumers through the examination of measurement invariance (MI) in CBEC environments. By using multiple-group confirmatory factor analysis (MG-CFA), this study explored the invariance of two product information cognitions on CBEC platforms, namely product description and product awareness, among consumers in North America, Europe, Latin America and Oceania. Data was collected from users of a popular CBEC platform in China. We find no significant differences in understandings and levels of awareness of product information across the four groups of consumers.

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“…This dimensionality reduction strategy mainly transforms multiple indices into a few composite indices: the original data are reduced in dimensionality; then, the useful indices are selected and imported to the algorithm, improving the analysis accuracy. The PCA was selected to preprocess the original data in our research, because the data involve five primary indices (products, users, traffic, sales and basic attribute), each of which contains multiple closely coupled secondary indices [14,15].…”

Section: Pcamentioning

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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Demand effects of product similarity network in e-commerce platform

Cited by 11 publications

References 65 publications

A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce

A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce

A Cross-Continent Analysis of the Invariance of Product Information in Cross-Border Electronic Commerce

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

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