  Autoencoders are popular among neural-network-based matrix completion models
due to their ability to retrieve potential latent factors from the partially
observed matrices. Nevertheless, when training data is scarce their performance
is significantly degraded due to overfitting. In this paper, we mit- igate
overfitting with a data-dependent regularization technique that relies on the
principles of multi-task learning. Specifically, we propose an
autoencoder-based matrix completion model that performs prediction of the
unknown matrix values as a main task, and manifold learning as an auxiliary
task. The latter acts as an inductive bias, leading to solutions that
generalize better. The proposed model outperforms the existing
autoencoder-based models designed for matrix completion, achieving high
reconstruction accuracy in well-known datasets.
