Rating prediction of recommended item based on review deep learning and rating probability matrix factorization

Zhiguo Zhu, Mengru Yan, Xiaoyi Deng, Ming Gao

Abstract: Highlights•Combines the Deep Learning for review texts and the Probability Matrix Factorization method for rating data to predict the rating of the recommended items accurately.•A deep learning framework of RNN with bi-directional GRU was designed to learn deep and nonlinear features of user preference and item characteristics from review documents.•Experimental results validate that the proposed model performed better than the other state-of-the-art models.•The proposed model has obvious cold start alleviation effects of the integrated review texts.