摘要
针对数字图书馆资源增加致使用户难以获取感兴趣图书资源的问题,提出了一种基于标签和关联规则挖掘的图书组合推荐系统模型。该模型整合了基于内容推荐和协同过滤推荐的优点,利用标签系统对图书内容进行语义分析,使用关联规则挖掘技术发现相似用户,并设计了组合推荐模型各功能模块结构及其算法。实验结果表明,组合推荐模型与算法优于其他图书推荐算法,获得了较高的推荐准确性。
The huge increase of digital library resources makes users＇ difficulty of accessing interested books becoming much higher. This paper proposed a hybrid recommendation system model based on tags and association rules mining for books. The system integrated the merits of content-based recommendation and collaborative recommendation,used tags system to analyze the semantic of books＇ contents,and utilized the association rules mining technique to discover the similar users. The paper designed each function module structure and its algorithm. The experimental results show that the presented recommendation model and algorithm is superior to other recommendation algorithm for books and can be obtained a higher predictive accuracy.
出处
《计算机应用研究》
CSCD
北大核心
2014年第8期2390-2393,共4页
Application Research of Computers
基金
国家"973"计划资助项目(2012CB724106)
国家文化部科技创新项目(2013KJCXXM13)
关键词
组合推荐
基于内容
协同过滤
标签
关联规则挖掘
hybrid recommendation
content-based
collaborative filtering
tag
association rules mining
