%0 Journal Article %A CHEN Hai %A QIAN Fulan %A CHEN Jie %A ZHAO Shu %A ZHANG Yanping %T Rating Prediction Model Based on Variational Auto-Encoder %D 2021 %R 10.3778/j.issn.1002-8331.2006-0440 %J Computer Engineering and Applications %P 153-159 %V 57 %N 22 %X

The deep learning model has the limitation of poor robustness. For example, the addition of specific noise in the picture will affect the classification and prediction results of the picture. Recently, some scholars have introduced deep learning into the recommendation system, so the influence of noise on the recommendation accuracy is also a problem in the recommendation system. Regarding the robustness of the deep recommendation model, this paper proposes a new rating prediction model REcommender Variational Auto-Encoder(REVAE) based on Variational Auto-Encoder(VAE). In order to train the robustness of the model to noise interference, a layer of hidden layer representation is added on the traditional VAE, the posterior distribution is used to constrain the hidden layer representation, and the noise is added on the hidden layer. By reconstructing the input data, the recommended algorithm model with anti-noise ability is trained. The experimental results on the public Movielens dataset show that REVAE can effectively reduce the noise interference to the model, making the whole model more robust and having better recommendation effect than other rating prediction algorithms.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2006-0440