Great paper! How do you guys deal with new users and new items with little or zero historical data? Seems like the model wouldn't have good latent factors for them
Thanks! This model handles new users gracefully because it can fallback to demographic/geographic priors and gradually specialize as the user watches videos. New items are difficult because of the fixed output vocabulary and batch training. In practice, this model is best suited for the head of the distribution and other specialized recommenders handle extremely fresh/low viewcount items. Feature engineering is key for new content during the ranking phase.