Joint Optimization of Ranking and Calibration (JRC) for CTR Prediction

We propose a hybrid generative/discriminative model called Joint Optimization of Ranking and Calibration (JRC) that simultaneously improves ranking ability and calibration accuracy for click‑through‑rate (CTR) prediction in advertising.

Traditional industrial models optimize pointwise LogLoss for calibration, while ranking metrics such as AUC/GAUC are optimized separately. JRC increases model degrees of freedom by outputting two logits (click and non‑click) and designs a logits interaction that replaces the single‑logit sigmoid with a softmax, preserving calibration via LogLoss and enhancing ranking via a listwise loss that pushes positive logits above negatives within the same session.

The final loss combines the calibrated LogLoss term and the listwise ranking term, weighted by a hyper‑parameter that is empirically insensitive. The approach can be interpreted as optimizing a mixed generative/discriminative objective, where the discriminative part enforces calibration and the generative part expands the logits to improve ranking.

Extensive offline experiments on Alibaba’s display‑ad dataset show that JRC outperforms pointwise, combined (pointwise + pairwise/listwise), and multi‑task baselines: it achieves higher GAUC while maintaining or improving LogLoss. Online A/B tests during the 618 shopping festival demonstrate significant gains (RPM +2.3 %, CTR +4.4 %, PPC ‑2.1 %, ROI +2.8 %) and a GAUC increase of 0.3 %.

Analysis reveals that JRC especially benefits sparse‑long‑tail users, where the additional generative supervision provides stronger signals. The model has been fully deployed across all Alibaba‑Mama display‑ad scenarios.

Future work includes extending the two‑logit formulation to deeper generative models, semi‑supervised learning, and uncertainty estimation.