AIE: Auction Information Enhanced Framework for CTR Prediction in Online Advertising

TL;DR

This paper introduces AIE, a framework that enhances CTR prediction in online advertising by better utilizing auction signals and addressing auction bias, leading to significant performance improvements.

Contribution

AIE proposes two lightweight, model-agnostic modules, AM2 and BCM, to effectively exploit auction information and mitigate bias in CTR prediction models.

Findings

AIE improves eCPM by 5.76% in online tests.

AIE enhances CTR by 2.44% in online tests.

Experimental results confirm AIE's effectiveness on public and industrial datasets.

Abstract

Click-Through Rate (CTR) prediction is a fundamental technique for online advertising recommendation and the complex online competitive auction process also brings many difficulties to CTR optimization. Recent studies have shown that introducing posterior auction information contributes to the performance of CTR prediction. However, existing work doesn't fully capitalize on the benefits of auction information and overlooks the data bias brought by the auction, leading to biased and suboptimal results. To address these limitations, we propose Auction Information Enhanced Framework (AIE) for CTR prediction in online advertising, which delves into the problem of insufficient utilization of auction signals and first reveals the auction bias. Specifically, AIE introduces two pluggable modules, namely Adaptive Market-price Auxiliary Module (AM2) and Bid Calibration Module (BCM), which work collaboratively to excavate the posterior auction signals better and enhance the performance of CTR prediction. Furthermore, the two proposed modules are lightweight, model-agnostic, and friendly to inference latency. Extensive experiments are conducted on a public dataset and an industrial dataset to demonstrate the effectiveness and compatibility of AIE. Besides, a one-month online A/B test in a large-scale advertising platform shows that AIE improves the base model by 5.76% and 2.44% in terms of eCPM and CTR, respectively.