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.

先前的研究大多将异常值视为噪声数据并消除它们，从而导致异常值知识的丢失。本研究基于现有的推荐系统和离群点检测技术，从离群点知识管理的角度开发了一种新的电子商务推荐模型。具体来说，我们应用离群数据挖掘并将局部离群系数整合到推荐算法中。实验结果表明，所提出的离群范围推荐模型的性能优于基于协同过滤算法的传统推荐系统，能够有效提高推荐质量，提升客户满意度和忠诚度，为企业创造潜在收益。