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Design of electronic-commerce recommendation systems based on outlier mining

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Abstract

Prior studies mostly consider outliers as noise data and eliminate them, resulting in the loss of outlier knowledge. Based on the existing technology of recommendation systems and outlier detection, this research develops a new e-commerce recommended model from the perspective of outlier knowledge management. Specifically, we apply outlier data mining and integrate local outlier coefficients into the recommendation algorithm. The experimental results show that the proposed outlier extent recommendation model performs better than the traditional recommendation systems based on the collaborative filtering algorithm, which can effectively improve the quality of recommendation, enhance customer satisfaction and loyalty, and create potential benefits for the business. Our study contributes to the design of e-commerce recommending systems with some novel ideas and provides useful guidelines for developing the outlier extent.

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Acknowledgement

This research has been supported by the National Natural Science Foundation of China: NSFC,71871172, Model of Risk knowledge acquisition and Platform governance in FinTech based on deep learning; NSFC,71571139, Outlier Analytics and Model of Outlier Knowledge Management in the context of Big Data. We deeply appreciate the suggestions from fellow members of Xia's project team and Research Center of Enterprise Decision Support, Key Research Institute of Humanities and Social Sciences in Universities of Hubei Province (DSS20200700).

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Authors and Affiliations

  1. School of Management, Wuhan Textile University, Wuhan, 430073, China

    Huosong Xia, Xiang Wei, Wuyue An & Zelin Sun

  2. Coggin College of Business, University of North Florida, Jacksonville, FL, 32224, USA

    Zuopeng Justin Zhang

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  1. Huosong Xia
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  2. Xiang Wei
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  3. Wuyue An
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Correspondence to Zuopeng Justin Zhang.

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Xia, H., Wei, X., An, W. et al. Design of electronic-commerce recommendation systems based on outlier mining. Electron Markets 31, 295–311 (2021). https://doi.org/10.1007/s12525-020-00435-2

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