| Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems |
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| 作者姓名： | YAO Yu ZHU Shanfeng CHEN Xinmeng |
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| 作者单位： | [1]School of Computer, Wuhan University, Wuhan430072, Hubei, China [2]Institute for Chemical Research, Kyoto University,Kyoto 611-0011, Japan |
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| 摘 要： | In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.
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| 关 键 词： | 肯德尔相关 协作滤除算法 正相关 新委任系统 |
| 文章编号： | 1007-1202（2006）05-1086-05 |
| 收稿时间： | 2006-03-25 |
Collaborative filtering algorithms based on Kendall correlation in recommender systems |
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| YAO Yu ZHU Shanfeng CHEN Xinmeng.Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems[J].Wuhan University Journal of Natural Sciences,2006,11(5):1086-1090. |
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| Authors: | Yao Yu Zhu Shanfeng Chen Xinmeng |
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| Institution: | (1) School of Computer, Wuhan University, 430072 Wuhan, Hubei, China;(2) Institute for Chemical Research, Kyoto University, 611-0011 Kyoto, Japan |
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| Abstract: | In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall
correlation method is used to measure the correlation amongst users by means of considering the relative order of the users'
ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another
discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and
Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.
Foundation item: Supported by the National Natural Science Foundation of China (60573095)
Biography: YAO Yu (1974-), female, Ph. D. candidate, Lecturer, research direction: information retrieval, distributed computing |
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| Keywords: | Kendall correlation collaborative filtering algorithms recommender systems positive correlation |
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