  How can we predict missing values in multi-dimensional data (or tensors) more
accurately? The task of tensor completion is crucial in many applications such
as personalized recommendation, image and video restoration, and link
prediction in social networks. Many tensor factorization and neural
network-based tensor completion algorithms have been developed to predict
missing entries in partially observed tensors. However, they can produce
inaccurate estimations as real-world tensors are very sparse, and these methods
tend to overfit on the small amount of data. Here, we overcome these
shortcomings by presenting a data augmentation technique for tensors. In this
paper, we propose DAIN, a general data augmentation framework that enhances the
prediction accuracy of neural tensor completion methods. Specifically, DAIN
first trains a neural model and finds tensor cell importances with influence
functions. After that, DAIN aggregates the cell importance to calculate the
importance of each entity (i.e., an index of a dimension). Finally, DAIN
augments the tensor by weighted sampling of entity importances and a value
predictor. Extensive experimental results show that DAIN outperforms all data
augmentation baselines in terms of enhancing imputation accuracy of neural
tensor completion on four diverse real-world tensors. Ablation studies of DAIN
substantiate the effectiveness of each component of DAIN. Furthermore, we show
that DAIN scales near linearly to large datasets.
