Collaborative filtering

as the core technology of recommendation systems

is currently facing the sparsity problem of rating data. This can be effectively solved through integrating item text information. However

current methods focus on extracting the one-dimensional features of the text

neglecting its multidimensional semantic features. Digging deeply into the multidimensional semantic features of the text can improve the recommendations. To help achieve this goal

a probabilistic matrix factorization model based on multidimensional semantic representation learning is proposed in the present study. The model uses a capsule network to mine the multidimensional semantic features of the text

and then integrates it into the probabilistic matrix decomposition framework using the regularization method to reveal hidden features linking users and items. Experimental results show that the proposed model has higher prediction accuracy.