An Interactive Perception Method Based Collaborative Rating Prediction Algorithm

To solve the rating prediction problems of low accuracy and data sparsity on different datasets, we propose an interactive perception method based collaborative rating prediction algorithm named DCAE-MF, by fusing dual convolutional autoencoder (DCAE) and probability matrix factorization (PMF). Deep latent representations of users and items are captured simultaneously by DCAE and are deeply integrated with PMF to collaboratively make rating predictions based on the known rating history of users. A global multi-angle collaborative optimization learning method is developed to effectively optimize all the parameters of DCAE-MF. Extensive experiments are performed on seven real-world datasets to demonstrate the superiority of DCAE-MF on key rating accuracy metrics of the root mean squared error (RMSE) and mean absolute error (MAE).