  We propose a novel filter for sparse big data, called an integrated
autoencoder (IAE), which utilizes auxiliary information to mitigate data
sparsity. The proposed model achieves an appropriate balance between prediction
accuracy, convergence speed, and complexity. We implement experiments on a GPS
trajectory dataset, and the results demonstrate that the IAE is more accurate
and robust than some state-of-the-art methods.
