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	基于不同数据集的协作过滤算法评测
	董丽 ; 邢春晓 ; 王克宏 ; DONG Li ; XING Chunxiao ; WANG Kehong
	2010-06-09 ; 2010-06-09
关键词	协作过滤 个性化推荐 算法评测 collaborative filtering personalized recommendation algorithm evaluation TP391.4
其他题名	Collaborative filtering algorithm evaluation for various datasets
中文摘要	针对协作过滤算法评测中普遍采用单一数据集,该文将传统的User-based(近邻数为20)、Item-based、Itemaverage、Item user average和Slope One 5种算法应用于MovieLens和Book-Crossing两种数据分布特征不同的数据集。结果显示,在Movielens这种评分值相对比较稠密的数据集上,Slope One算法的预测精度最好;而在评分值相对比较稀疏的Book-Crossing数据集上,Item-based算法的预测精度最好,Slope One的预测精度最差。选择算法应根据用户和资源分布具体情况确定。; Most collaborative filtering(CF) research has focused on doing experiments on single dataset or datasets with the same characteristics.This paper presents an analysis of several typical CF algorithms,the User-based KNN method(with 20 neighborhoods),the item-based method,the item average method,the item user average method,and the Slope One method.These algorithms are evaluated on two types of datasets,Movielens and Book-Crossing,which have different user-item distribution characteristics.The results show for the relatively dense ratings on the Movielens dataset,the Slope One method has the best prediction precision,while on datasets with relatively sparse ratings such as Book-Crossing,the item-based method is the best,while the Slope One method is the worst.Thus,the different CF algorithms give different results on the different datasets,so the CF algorithm should be designed according to the user-item distribution characters.; 国家自然科学基金资助项目(60473078); 国家“八六三”高技术项目(2006AA010101); 国家“十一五”科技支撑计划资助项目(2006BAH02A12)
语种	中文 ; 中文
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/55741]
专题	清华大学
推荐引用方式 GB/T 7714	董丽,邢春晓,王克宏,等. 基于不同数据集的协作过滤算法评测[J],2010, 2010.
APA	董丽,邢春晓,王克宏,DONG Li,XING Chunxiao,&WANG Kehong.(2010).基于不同数据集的协作过滤算法评测..
MLA	董丽,et al."基于不同数据集的协作过滤算法评测".(2010).

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