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计算机系统应用英文版:2010,19(5):183-186
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一种基于分众分类的协同过滤推荐算法
(中国科学技术大学 管理科学系 安徽 合肥 230026)
A Collaborative Filtering Recommendation Algorithm Based on Folksonomy
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Received:September 15, 2009    Revised:December 08, 2009
中文摘要: 基于内存的协同过滤算法是推荐系统中使用的最成功的技术之一,但它存在着数据稀疏性和可扩展性的问题。分众分类是一种能使用户发现、组织和理解在线事物的强有力的机制。基于这种机制,提出了一种新的协同过滤算法,来解决该算法中的稀疏性和可扩展性的问题。实验表明,该算法在解决这些问题上是有效的。
Abstract:The memory-based collaborative filtering algorithm is one of the most successful technologies for recom- mender systems, although these approaches all suffer from data sparsity and poor scalability problems. Folksonomy has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. This paper focuses on how to address these problems by using tags. The experiment for the tag-based algorithm and other algorithms showed that the novel algorithm achieves better performance compared to the traditional ones, proving the validity of the algorithm.
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吴春旭,李佳俊,石辉.一种基于分众分类的协同过滤推荐算法.计算机系统应用,2010,19(5):183-186
WU Chun-Xu,LI Jia-Jun,SHI Hui.A Collaborative Filtering Recommendation Algorithm Based on Folksonomy.COMPUTER SYSTEMS APPLICATIONS,2010,19(5):183-186