基于社群挖掘的用户个性化信息推荐方法研究

被引：5

作者：

余以胜 ^{[1
]}

徐剑彬 ^{[2
]}

刘鑫艳 ^{[1
]}

机构：

[1] 华南师范大学经济与管理学院

[2] 顺丰控股(集团)股份有限公司

来源：

情报学报 | 2017年 / 36卷 / 10期

关键词：

社群挖掘; 个性化推荐; 情报学科建设;

D O I：

暂无

中图分类号：

TP391.3 [检索机];

学科分类号：

摘要：

当前情报学科的发展目前呈现出多维度、跨学科等特点,而结合个性化信息推荐算法,可为其注入新活力。本文的研究是为了提高个性化信息推荐的效果,解决个性化信息推荐的稀疏性问题,以期可以促进情报学科的新发展,为此,我们引入了社群挖掘概念,得到TO算法,在协同过滤或关联规则推荐之前先对数据进行社团划分,通过对Book-crossing公开数据集的验证分析,并与对照算法相比,我们发现TO算法的准确率和调和度都最佳。

引用

页码：1093 / 1098

页数：6

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