使用正态分布函数修正推荐系统相关相似性

为提高协同过滤推荐系统寻找最近邻居集合的准确程度，在传统的相关相似性基础上，提出了一种利用正态分布函数作为修正函数的相关相似性计算方法，该方法依据双方共同评分的项目进行用户相似性评价，利用正态分布函数来修正用户之间评分项目数差距对相关相似性计算产生的负面影响，能够较好地体现用户的相似程度。实验结果表明，在相同条件下，该方法与传统的相关相似性计算方法，在一定程度上提高了寻找最近邻居用户集合的准确度。

Correction of the Correlation Similarity in Recommendation Systems Using the Normal Distribution Function

A calculation method of correlation similarity with normal distribution function as a correction function is proposed based on the traditional correlation for better accuracy of seeking the nearest neighbor set in the collaborative filtering recommendation system. The normal distribution function is employed to correct the negative effect because of the number of items between users on similarity calculation, which well reflects the degree of similarity of users. The experimental results show that the proposed method offer better accuracy of searching for the nearest neighbor set than the traditional method of correlation similarity under the same conditions.