摘要
针对目前传统数字图书馆无法为用户提供准确个性的图书推荐服务的问题,提出构建基于协同过滤的图书智能推荐系统。首先对图书进行聚类,构建无缺失的图书评价矩阵,在此基础上根据读者对相似图书的评分预测读者的兴趣爱好,为读者提供个性化的图书推荐。该方法在评分数据极端稀疏的情况下也可以为读者作出准确的图书推荐。最后通过实验验证该推荐方法的有效性和实用性。
In regard to the impossibility of recommending accurate and individualized book through traditional digital libraries, the paper puts forth the construction of intelligent and collaborative filtering-based book recommendation. At first, books are clustered to construct complete evaluative matrix, then predict readers＇ hobbies after they score similar books, and provide individual recommenda- tion of books for readers. It is possible to do so even in case that the scoring data are rare. The experiment has proven it efficient and practical.
出处
《图书情报工作》
CSSCI
北大核心
2011年第1期35-38,共4页
Library and Information Service
关键词
数据挖掘
协同过滤
图书推荐
data mining collaborative filtering book recommendation
