With the popularity of the Internet and the accumulation of large amounts of data

recommendation system

as an effective means to solve the problem of information overload

can help people quickly select what they are interested in.Because of the sparse user-item rating data

and the cold start problem of new users or new items

traditional recommendation algorithm has the shortcoming of high complexity

low accuracy.Considering the accumulated users behavior or rating data across different domains can have the same preferences

we can share the knowledge pattern among different domains.Based on the matrix factorizationof user-itemrating data in different domains

we can obtain the latent feature matrix of users and items respectively.Considering the user group preference

the latent features of users and items are clustered separately as the domainknowledge pattern.Moreover

By clustering the cross-domain knowledge patterns

we can get shared common knowledge pattern.With the domain knowledge pattern and the shared common knowledge pattern

we can make the finalrecommendation.Based on the above consideration

this paper proposes the SKP (Sharing Knowledge Pattern)algorithm.And the SKP is realized in a parallel manner.Experimentsare carried out in the physical cluster environment.By exploiting three different datasets

the results show that the SKP algorithm has better recommendation accuracy and lower RMSE values compared with the existing single-domain algorithm and other cross-domainalgorithms.