CPM-sensitive AUC for CTR prediction
This addresses a critical problem for advertising platforms by reducing waste in online traffic and human costs, though it is incremental as it modifies an existing evaluation metric.
The paper tackles the gap between offline AUC and online CPM in CTR prediction for advertising, proposing CPM-sensitive AUC (csAUC) to better align with revenue metrics, with a calculation method based on dynamic programming for large-scale data.
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.