In the current social recommendation system

user roles are typically not dynamically annotated based on changed user interest. The flaw may lead to the prediction inaccuracy of recommendation. Besides

sparsity of user rating data can also cause imprecise prediction. According to the above problems

this paper proposes a recommendation model based on dynamic role identification and tensor decomposition. Firstly

when user roles are quantitatively identified

information entropy is used to capture the diversity of user interest for solving the problem of indiscriminate user role identification. Secondly

considering user interest drifting

the dynamic role identification method based on time window is proposed

which enables the preference difference of individual rating data generated by static role identification and realizes the hierarchical processing of user rating data. Finally

a rating prediction model based on "user-item-role" tensor decomposition is constructed. And the characteristics of tensor in data dimension transformation and data compression is introduced into the model. In addition

by dealing with the missing value

accuracy of rating prediction is improved. Experiments demonstrate that this model can alleviate inaccurate prediction caused by indiscriminate identity of user role

and can effectively improve recommendation performance compared with the traditional recommendation model.