A weight-based item recommendation approach for electronic commerce systems

Zhao, Yingsi; Liu, Yan-Ping; Zeng, Qing-An

doi:10.1007/s10660-015-9188-1

Cited by 20 publications

(10 citation statements)

References 22 publications

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“…After getting classification experts, it is necessary to determine their weights (Zhao et al, 2015). In this paper, experts give the prediction of consumer satisfaction based on the different consumer needs and demographic characteristics.…”

Section: Measurement Of Expert Weightmentioning

confidence: 99%

A new recommendation system on the basis of consumer initiative decision based on an associative classification approach

Yin

Guo

Yang³

et al. 2018

IMDS

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Purpose The purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system. Design/methodology/approach By employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop. Findings The results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision. Originality/value Conventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.

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Section: Measurement Of Expert Weightmentioning

confidence: 99%

A new recommendation system on the basis of consumer initiative decision based on an associative classification approach

Yin

Guo

Yang³

et al. 2018

IMDS

View full text Add to dashboard Cite

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“…Constructing a more effective non-linear mapping function to reduce the amount of data may help to overcome this limitation. (Zhao et al, 2015) 3)…”

Section: )mentioning

confidence: 99%

“…(Zhao et al, 2015) Therefore, Hybrid recommendation techniques emerged. Hybrid recommendation techniques combine two or more recommendation methods, such as CB and CF based techniques in order to avoid certain limitations such as cold-start problem, while improving RS performance.…”

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confidence: 99%

A Survey of e-Commerce Recommender Systems

Karimova

2016

ESJ

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Due to their powerful personalization and efficiency features, recommendation systems are being used extensively in many online environments. Recommender systems provide great opportunities to businesses, therefore research on developing new recommender system techniques and methods have been receiving increasing attention. This paper reviews recent developments in recommender systems in the domain of ecommerce. The main purpose of the paper is to summarize and compare the latest improvements of e-commerce recommender systems from the perspective of e-vendors. By examining the recent publications in the field, our research provides thorough analysis of current advancements and attempts to identify the existing issues in recommender systems. Final outcomes give practitioners and researchers the necessary insights and directions on recommender systems.

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“…These factors may alienate customers and impede the sustainable development of e-business. As an information technology to resolve the above problems [3,4], the recommendation system is widely applied by e-commerce practitioners and has become an important research topic in information science and decision-support systems [5,6]. Currently, the research on recommendation systems generally includes content-based filtering (CBF) [7], collaborative filtering (CF) [8,9], and other data-mining techniques [10], such as decision trees [11,12], association rules [13], and the semantic approach [14].…”

Section: Introductionmentioning

confidence: 99%

Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business

Guo

Yin²,

Li³

et al. 2018

Sustainability

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A lack of in-depth excavation of user and resources information has become the main bottleneck restricting the predictive analytics of recommendation systems in mobile commerce. This article provides a method which makes use of multi-source information to analyze consumers' requirements for e-commerce recommendation systems. Combined with the characteristics of mobile e-commerce, this method employs an improved radial basis function (RBF) network in order to determine the weights of recommendations, and an improved Dempster-Shafer theory to fuse the multi-source information. Power-spectrum estimation is then used to handle the fusion results and allow decision-making. The experimental results illustrate that the traditional method is inferior to the proposed approach in terms of recommendation accuracy, simplicity, coverage rate and recall rate. These achievements can further improve recommendation systems, and promote the sustainable development of e-business.

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A weight-based item recommendation approach for electronic commerce systems

Cited by 20 publications

References 22 publications

A new recommendation system on the basis of consumer initiative decision based on an associative classification approach

A new recommendation system on the basis of consumer initiative decision based on an associative classification approach

A Survey of e-Commerce Recommender Systems

Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business

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