Collaborative Filtering with User-Item Co-Autoregressive Models

Understand

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.