MARCO: Click-Intent Decomposition for Calibrated Ads Conversion Prediction

2026-08-11

AI Summary

This work addresses a critical limitation in conventional conversion rate (CVR) prediction models, which treat all clicks as homogeneous events and thereby overlook the heterogeneity of user click intent—leading to underestimation of high-intent clicks and overestimation of low-intent ones. To remedy this, the study introduces a novel click-intent disentanglement framework that leverages interface interaction signals (e.g., click type) as proxy labels for intent, enabling the construction of multiple intent-specific CVR sub-models. The final prediction is dynamically fused based on the estimated intent distribution. The authors further propose an end-to-end consistency constraint and a first-click–last-impression credit assignment mechanism to resolve attribution ambiguity in multi-impression, multi-click scenarios. Online experiments demonstrate near-perfect calibration accuracy across intent segments, a 2.80% uplift in per-click conversion, and a cumulative 0.98% improvement in core business metrics.

Abstract

Not all clicks are equal. Industrial ads ranking decouples conversion probability into click-through rate (CTR) and post-click conversion rate (CVR), yet treats every click as the same event. In reality, users provide a free, self-generated signal of intent through their physical UI interactions. Different click types on the same ad exhibit a 4-fold difference in actual conversion rates. By conflating these signals, the standard CVR model under-predicts high-intent clicks and over-predicts low-intent ones, which is a bias masked by near-perfect aggregate calibration. We propose MARCO (Multi-intent Ads Ranking Composition Optimization), a framework that resolves this bias by decomposing each click by intent. Using the logged click type as a free behavioral label, MARCO trains per-intent CVR heads on homogeneous populations, and at serving time composes their per-intent CVR estimates under a predicted distribution over intents. Theoretically, we prove that decomposition never raises population risk, give the exact headroom under squared loss and non-negativity under the deployed loss, and show through a routing-efficiency dial how much of it reaches serving. Because the population-optimal score is unchanged, any gain is a finite-capacity estimation and calibration effect that we validated both offline and online. For deployment at scale, we further cast multi-impression, multi-click attribution as credit assignment with a bias-variance tradeoff analogous to RL return estimation, showing last-impression, first-click attribution is the low-bias, low-variance, deterministic choice under production constraints, and derive three consistency conditions enforced end-to-end at scale. Deployed at binary intent granularity, MARCO corrects per-intent calibration to approximately 100%, lifts conversions per click by +2.80%, and drives +0.98% cumulative improvement in topline metrics.

Problem

• click-intent
• conversion prediction
• calibration bias
• CVR estimation
• user intent

Innovation

• click-intent decomposition
• conversion rate calibration
• multi-head CVR modeling
• credit assignment in ad attribution
• intent-aware ranking