摘要
针对传统协同过滤算法依赖单一用户需求形态影响推荐效果的问题,提出一种基于用户多态聚类的数字图书馆个性化推荐方法。该方法以改进的海明距离计算候选邻居集,结合多态相似度进行二次聚类,预测用户的多态需求度并形成推荐。实验表明,使用多态聚类产生的推荐精确度上优于单一聚类产生的推荐。
Traditional collaborative filtering algorithm is usually dependent on single kind of user requirement to generate clustering and this may affect the accuracy of recommendation.In view of the problem,this paper proposes a personalized recommendation method in digital library based on users' polymorphic clustering.This method uses an improved Hamming distance to calculate candidate neighbors,then combines polymorphic similarity to cluster again,finally forecasts user's requirements degree and generates recommendation.The experiments show that recommendation based on polymorphic clustering is more accurate than the single's.
出处
《现代图书情报技术》
CSSCI
北大核心
2012年第2期18-22,共5页
New Technology of Library and Information Service
基金
华侨大学科研基金项目"基于用户需求模型的个性化信息服务研究"(项目编号:10HJY06)的研究成果之一
关键词
数字图书馆
个性化推荐
多态性
协同过滤
Digital library Personalized recommendation Polymorphism Collaborative filtering