Transformation structured to link user/item avg. rating with sentiment probability.
A model is proposed to combine HFT with ASUM based on the transformation.
Latent factors are linked with topic probabilities to model aspects of sentiments.
An optimization algorithm is given to optimize the parameters of RAS alternately.
Prediction accuracy and convergence rate are demonstrated on real-world datasets.