作　　者： ;

机构地区： 广东技术师范学院计算机科学学院

出　　处： 《微电子学与计算机》 2011年第5期93-96,共4页

摘　　要： 由于标签的灵活性及其概念可理解性,使用标签可以提高推荐系统的推荐性能.协同标签系统在网络资源推荐服务中取得了巨大的成功.分类为用户显示了不同的利益群体的不同喜好.基于此,提出了基于分类的标签推荐系统—TRSUC,将它作为内分类推荐,使分类标签成为全球用户和项目之间的中介实体.通过对MovieLens中数据集进行实验,结果表明,TRSUC的推荐准确度明显优越于传统推荐算法. Collaborative tagging system has become more and more popular and recently achieved widespread success due to flexibility and conceptual comprehensibility of tagging systems.Recommender system has the access to adopt tagging systems to achieve better performance.In this paper we consider that the items can be categorized into different classifications in which users show different interests.Here we adopt a two-step recommender method called TRSUC（Tagging Recommender Systems by Using Classification） which can be described as Inner-Class Recommender or Global Recommender in which we use tag as the intermediary entity between user and item.The experiment using MovieLens as dataset shows that we acquire better results than the recommender algorithms without classifying the items.

关 键 词： 协同标签 推荐系统 分类 准确度

领　　域： [自动化与计算机技术] [自动化与计算机技术]