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中文摘要：
提出了一个新的相似度概念——元相似度,并在此基础上对标准的协同过滤算法进行了改进。元相似度即相似度的相似度,与相似度相比元相似度是基于相似度矩阵而不是相关矩阵计算得出的。即使是在相关矩阵中未购买过任何相同商品的两个用户也可以用元相似度反映他们之间的相似关系,这样在一定程度上解决了冷启动和矩阵稀疏性问题。综合考虑元相似度与相似度可以更好地刻画两个用户之间的相关关系,从而得到更精准的预测。大量的实验模拟表明,提出的算法在ranking score、precision和recall等重要的精度指标上都取得了显著的提高。
英文摘要：
This paper presented a collaborative filtering algorithm based on a new similarity definition,namely meta similarity.By considering the vector correlation of the user similarity matrix,the meta similarity between any two users could be obtained.Even the relation of two users who had no common collected items could be investigated by it.Combining with the initial similarity with a tunable parameter,the integrated similarity could reflect relations between users more properly.Numerical results indicate that the algorithmic accuracy,measured by the average ranking score,and precision and recall is improved greatly.
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