# CPM-sensitive AUC for CTR prediction

## TL;DR

This paper introduces CPM-sensitive AUC (csAUC), a new evaluation metric for CTR prediction that aligns offline evaluation with online CPM performance, addressing the gap between traditional AUC and real-world advertising revenue.

## Contribution

The paper proposes a novel csAUC metric that accounts for bid differences and online CPM, with an efficient calculation method suitable for large-scale data.

## Key findings

- csAUC better correlates offline metrics with online CPM performance
- The calculation method supports large-scale real-world data
- Addresses the offline-online evaluation gap in CTR prediction

## Abstract

The prediction of click-through rate (CTR) is crucial for industrial applications, such as online advertising. AUC is a commonly used evaluation indicator for CTR models. For advertising platforms, online performance is generally evaluated by CPM. However, in practice, AUC often improves in offline evaluation, but online CPM does not. As a result, a huge waste of precious online traffic and human costs has been caused. This is because there is a gap between offline AUC and online CPM. AUC can only reflect the order on CTR, but it does not reflect the order of CTR*Bid. Moreover, the bids of different advertisements are different, so the loss of income caused by different reverse-order pair is also different. For this reason, we propose the CPM-sensitive AUC (csAUC) to solve all these problems. We also give the csAUC calculation method based on dynamic programming. It can fully support the calculation of csAUC on large-scale data in real-world applications.