基于欧氏空间相似度的云模型协同过滤算法

传统的基于余弦相似度度量的云模型协同过滤推荐算法未考虑特征向量的长度和维度,忽略了三个重要数字特征云期望、熵和超熵的关系,如各数字特征具有不同的性质和权重,导致特征丢失、区分度过小的问题。针对这些问题,提出了一种采用标准化的多维欧几里德相似度计算方法,通过将三个数字特征映射为三维空间的点,计算经指数函数标准化的欧几里德相似度,生成更合理的用户k近邻集,最终产生推荐。实验结果表明,该相似度计算方法能够为云特征向量提供更显著的区分度,并在一定程度上提高了推荐质量。

A cloud model based collaborative filtering recommendation algorithm using Euclidean distance similarity measurement

Cosine similarity measurement method is one of the collaborative filtering recommendation algorithms based on cloud model, in which neither the length and dimension of feature vectors nor the relationship among the three digital features of cloud model (cloud expectation, entropy and hyper entropy) are taken into serious consideration.Digital features have different properties and weights, which leads to feature loss and lack of discrimination. Aiming at these problems, we propose a new method which uses Euclidean distance to measure the similarities of the cloud feature vectors.Cloud expectation,entropy and hyper entropy are mapped into the points in a three dimensional space,and the Euclidean similarities normalized by the exponential function are calculated,thus more proper k nearest neighbours sets are generated and recommendation results are obtained.Experimental results show that the new similarity measurement method can not only improve the differentiation of cloud feature vectors but also provide a better recommendation quality.