A Survey of Explainable E-Commerce Recommender Systems

被引:0
作者
Gao, Huaqi [1 ]
Zhou, Shunke [1 ]
机构
[1] York Univ, Sch Informat & Technol, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON, Canada
来源
2022 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WI-IAT | 2022年
基金
加拿大自然科学与工程研究理事会;
关键词
Recommender System (RS); e-commerce;
D O I
10.1109/WI-IAT55865.2022.00115
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Since the growing information overload has become more and more serious on the Web, especially in the field of e-commerce. The explainable recommender system plays a crucial role in providing users with explanations of recommendations to enhance customer satisfaction and loyalty. In recent years, various explainable recommender approaches have been proposed and applied in e-commerce. This survey will review the development of explainable recommender systems, existing methods to generate explainable recommendations, applications in the e-commerce field, and further discuss future directions that can be incorporated and implemented to improve the quality of explainable recommender systems.
引用
收藏
页码:723 / 730
页数:8
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