Information entropy based multi-criteria recommendation

被引：0

作者：

Li, Hui ^{[1
]}

Song, Xiangyu ^{[1
]}

Mao, Mingsong ^{[1
]}

Xue, Bingyu ^{[1
]}

机构：

[1] Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang 330013, Jiangxi, Peoples R China

来源：

DEVELOPMENTS OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN COMPUTATION AND ROBOTICS | 2020年 / 12卷

基金：

中国国家自然科学基金;

关键词：

recommender system; multi-criteria; recommendation method; criteria weight; Information entropy;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In recent years, many e-commerce sites allow users to evaluate multiple aspects of a product, which provides a more comprehensive user preference information for the recommendation system and helps improve the recommendation quality. Therefore, multi-criteria recommender systems have increasingly attracted researchers' interest in recent years. In this paper, we propose an information entropy based multi-criteria recommendation algorithm. The results from experiments based on Yahoo Movie dataset show that the new method can improve the recommendation accuracy of recommender systems.

引用

页码：513 / 521

页数：9

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