Design of electronic-commerce recommendation systems based on outlier mining

Huosong Xia (), Xiang Wei (), Wuyue An (), Zuopeng Justin Zhang () and Zelin Sun ()
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Huosong Xia: Wuhan Textile University
Xiang Wei: Wuhan Textile University
Wuyue An: Wuhan Textile University
Zuopeng Justin Zhang: University of North Florida
Zelin Sun: Wuhan Textile University

Electronic Markets, 2021, vol. 31, issue 2, No 5, 295-311

Abstract: 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.

Keywords: Electronic-commerce; Recommendation system; Outlier mining; Outlier extent model; Outlier factor; Local outlier Factor (search for similar items in EconPapers)
JEL-codes: C63 M15 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s12525-020-00435-2

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