Exploiting SDAE Model for Recommendations

The data for recommendations, usually a matrix composed of users and items, include a large number of missing data, noise data, etc, which have a negative effect on the accuracy of recommendations. In order to improve recommendation performance, this paper put forwards an improved model named Stacked Denoising AutoEncoder (SDAE), which improves the autoencoder by both indicators and denoising parts to construct an effective stacked autoencoding network. The first layer of the encoding network is responsible for dealing with missing data with the help of indicators and to get a new encoding for features, and then stacked denoising is applied to process noise data for a further optimization. SDAE's output can be accepted by collaborative filtering methods to provide a more accurate recommendation. Three data sets are used to verify the proposed model, the experimental results show that the proposed model presents an active ability on improving recommendation performance and mitigates the negative influence caused by missing data, noise data, etc.