Title

Design of electronic-commerce recommendation systems based on outlier mining

Document Type

Article

Publication Date

6-1-2021

Subject Area

ARRAY(0x5557d368d5b0)

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.

Publication Title

Electronic Markets

Volume

31

Issue

2

First Page

295

Last Page

311

Digital Object Identifier (DOI)

10.1007/s12525-020-00435-2

ISSN

10196781

E-ISSN

14228890

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