Recommendation Systems with Distribution-Free Reliability Guarantees

Understand

When building recommendation systems, we seek to output a helpful set of items to the user.

• Under the hood, a ranking model predicts which of two candidate items is better, and we must distill these pairwise comparisons into the user-facing output.
• However, a learned ranking model is never perfect, so taking its predictions at face value gives no guarantee that the user-facing output is reliable.
• Building from a pre-trained ranking model, we show how to return a set of items that is rigorously guaranteed to contain mostly good items.