IDAE: Imputation-boosted denoising autoencoder for collaborative filtering

In recent years, while deep neural networks have shown impressive performance to solve various recognition and classification problems, collaborative filtering (CF) received relatively little attention to utilize deep neural networks. Because of inherent data sparsity, it remains a challenging problem for deep neural networks. In this paper, we propose a new CF model, namely the imputation-boosted denoising autoencoder (IDAE), for top-N recommendation. Specifically, IDAE consists of two steps: imputing positive values and learning with imputed values. First, it infers and imputes positive user feedback from missing values. Then, the correlation between items is learned by using the denoising autoencoder (DAE) with imputed values. Unlike the existing DAE that randomly corrupts the input, the key characteristic of IDAE is that original user values are taken as the input, and imputed values are reflected as the corrupted output. Our experimental results demonstrate that IDAE significantly outperforms state-of-the-art CF algorithms using autoencoders (by up to 5%) on the MovieLens datasets.