Focusing on the issues of rating prediction such as subjectivity of user rating and inaccurate prediction caused by rating sparsity

a rating prediction method is proposed by introducing the characteristics of social recommendation.Firstly

aiming at the subjective of user rating

we introduce and optimize the cloud model theory.Then

a method to generate rating standard by synthetical cloud model and transform user rating under the standard is proposed.Secondly

to deal the problem of inaccurate score prediction caused by data sparseness

data dimension reduction and target user location are achieved by introducing membership degree.And taking into account that user rating can be affected by their social relationship

we try to learn two rating prediction models by respectively using social relationships and similar groups.Finally

the rating value is obtained by using Gauss transform to combine the two prediction models.Experimental results show that our method not only overcomes subjectivity of user rating

but also alleviates the poor accuracy caused by rating sparsity problem in traditional rating prediction methods.