CONCEPT Cited by 1 source
CTR prediction¶
Definition¶
CTR prediction (click-through rate prediction) is the machine-learning task of estimating the probability that a user will engage (click, like, watch, tap) with a specific candidate item (ad, content, link, Pin) in a specific context. It is the core scoring primitive under ads ranking and recommendation systems: given user, candidate, and context features, output a probability P(engage | user, candidate, context).
CTR prediction is typically trained on binary engagement labels (engaged / not engaged) at the impression-level, with log-loss or a calibrated classification objective. In multi-task / multi-label recsys architectures (see MTML ranking), CTR is often one of several task heads (CTR + like + share + follow, each with its own head) trained jointly.
Why it's load-bearing¶
The CTR score enters the final ranking formula and drives the auction mechanics in ads serving:
- Ad ranking. Candidate ads are ranked by
bid × predicted-CTR(or a more complex expected-value formula); higher-CTR ads win more auctions. - Calibration matters for the auction to be economically valid. Predicted probabilities must be close to empirical rates — overconfident CTR predictions bias budget spend to specific surfaces or ad formats and break revenue guarantees. See surface-specific calibration.
- Serving cost is multiplied by candidate count. Every ad candidate needs a CTR score, so CTR models are evaluated O(candidates) times per request — the scaling axis behind optimisations like request-level user-embedding broadcasting and surface-specific tower trees.
Canonical wiki instance¶
Pinterest's unified ads engagement model predicts CTR across three ads surfaces (Home Feed, Search, Related Pins) with surface-specific calibration as the critical correctness mechanism (Source: sources/2026-03-03-pinterest-unifying-ads-engagement-modeling-across-pinterest-surfaces):
"Since the unified model serves both HF and SR traffic, calibration is critical for CTR prediction. We found that a single global calibration layer could be suboptimal because it implicitly mixes traffic distributions across surfaces."
Seen in¶
- 2026-03-03 Pinterest — Unifying Ads Engagement Modeling (sources/2026-03-03-pinterest-unifying-ads-engagement-modeling-across-pinterest-surfaces) — CTR as the core prediction target unified across surfaces; calibration treated as first-class architectural concern.