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Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems 被引量:3

Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems
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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. 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.
出处 《Wuhan University Journal of Natural Sciences》 CAS 2006年第5期1086-1090,共5页 武汉大学学报(自然科学英文版)
基金 Supported by the National Natural Science Foun-dation of China (60573095)
关键词 Kendall correlation collaborative filtering algorithms recommender systems positive correlation Kendall correlation collaborative filtering algorithms recommender systems positive correlation
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