  Deep neural networks have shown promise in collaborative filtering (CF).
However, existing neural approaches are either user-based or item-based, which
cannot leverage all the underlying information explicitly. We propose CF-UIcA,
a neural co-autoregressive model for CF tasks, which exploits the structural
correlation in the domains of both users and items. The co-autoregression
allows extra desired properties to be incorporated for different tasks.
Furthermore, we develop an efficient stochastic learning algorithm to handle
large scale datasets. We evaluate CF-UIcA on two popular benchmarks: MovieLens
1M and Netflix, and achieve state-of-the-art performance in both rating
prediction and top-N recommendation tasks, which demonstrates the effectiveness
of CF-UIcA.
