Abstract:  As the autoencoders neural network can extract the features of data from low-level to high-level, and find the potential correlation between the samples, in order to improve the accuracy of the recommendation system, this paper proposes a recommendation algorithm based on denoising autoencoders. Firstly, this paper pretreats score data by ZCA whitening, adds the random noise in the processed data, and builds the autoencoders neural network, then obtains the network weights through the model pre-training, 〖JP2〗fine-tuning. Finally according to the trained network weights the test sample score is reconstructed to forcast user rating  and calculate score error. Experimental results show that denoising autoencoders can effectively improve the recommendation accuracy.

Key words: recommended system, deep learning, autoencoders neural network, ZCA whitening, denoising autoencoders