Abstract:

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. 

Key words: collaborative filtering;digital features of cloud model;Euclidean distance similarity;normalization