| 基于不同数据集的协作过滤算法评测 |
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| 引用本文： | 董丽,邢春晓,王克宏.基于不同数据集的协作过滤算法评测[J].清华大学学报(自然科学版),2009(4). |
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| 作者姓名： | 董丽 邢春晓 王克宏 |
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| 作者单位： | 清华大学计算机科学与技术系;清华大学图书馆;清华大学信息技术研究院; |
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| 基金项目： | 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) |
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| 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。
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| 关 键 词： | 协作过滤 个性化推荐 算法评测 |
Collaborative filtering algorithm evaluation for various datasets |
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| DONG Li,XING Chunxiao,WANG Kehong.Collaborative filtering algorithm evaluation for various datasets[J].Journal of Tsinghua University(Science and Technology),2009(4). |
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| Authors: | DONG Li XING Chunxiao WANG Kehong |
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| Institution: | 1.Department of computer Science and Technology;Tsinghua University;Beijing 100084;China;2.Tsinghua University Library;3.Research Institute of Information Technology;China |
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| Abstract: | 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 fo... |
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| Keywords: | collaborative filtering personalized recommendation algorithm evaluation |
| 本文献已被 CNKI 等数据库收录！ |
