摘要
现有的协同过滤推荐算法主要依据用户对资源的评分,但是通常用户的评分矩阵数据稀疏,少量的评分数据不能很好表示出用户和资源的特点。然而,标签既能反映用户的兴趣又能描述资源的自身特征。因此,本文通过引入标签,提出一种基于标签和协同过滤的个性化推荐算法。该算法将标签视为用户和资源的中间纽带,通过拆分用户-标签-资源三维关系图后分别计算用户与标签、标签与资源的关联度,构建用户的兴趣模型。再依据用户兴趣模型预测对于待推荐新资源的兴趣度,最终产生Top-N推荐集。在公开数据集Movie Lens上,与现有算法的比较结果表明,该算法在准确率、召回率上均有所提升,达到了更好的推荐效果。
Existing collaborative filtering algorithm is primarily based on user＇ s ratings for resources, but usually user ratings matrix data are sparse, a small amount of data can not express very well the characteristics of users and resources. However, the tag can reflect the user＇ s interest and can describe the features of the resource. Therefore, by introducing tag, a personalized recommendation algorithm based on tags and collaborative filtering is proposed in this paper. The basic idea is to consider the tag as an intermediate link between the user and resource. Then the correlation degree between user and tag, tag and resource is calculated through splitting three-dimensional relationship among the users, tags and resources, and then build the user＇ s interest model. Finally, according to the user＇ s interest model to predict preference values for other new resources, and produce the Top-N recommendation set. Compared with the existing algorithm, the precision and the recall rate of the algorithm proposed are all improved, and reach a better recommended effect.
出处
《计算机与现代化》
2016年第2期62-65,71,共5页
Computer and Modernization
关键词
标签
协同过滤
兴趣模型
个性化推荐
tags
collaborative filtering
interest model
personalized recommendation
