In order to verify whether the context environment in which users rate items would affect their preferences, according to the Markovian factorization of matrix processes method, a streaming recommendation algorithm based on context (C-SRA) is proposed. On the basis of this method, the selected context information is divided into subjective context and objective context, and the two types of context information and algorithm are integrated in turn. Finally, two groups of experiments based on LDOS-CoMoDa data set show that the C-SRA performed better than the other comparison algorithms in both rate prediction and the recommendation.

streaming data; context; matrix factorization; information gain; streaming recommendation algorithm