Guiding the Recommender: Information-Aware Auto-Bidding for Content Promotion

TL;DR

This paper introduces an information-aware auto-bidding framework for content promotion that balances short-term engagement with long-term model improvement, outperforming traditional methods in experiments.

Contribution

It proposes a novel dual-objective optimization approach with a decomposable surrogate, gradient coverage, and a two-stage auto-bidding algorithm incorporating confidence heuristics and theoretical guarantees.

Findings

Outperforms baseline methods in AUC and LogLoss metrics.

Achieves budget adherence and effective gradient estimation.

Enhances long-term content quality and recommendation performance.

Abstract

Modern content platforms offer paid promotion to mitigate cold start by allocating exposure via auctions. Our empirical analysis reveals a counterintuitive flaw in this paradigm: while promotion rescues low-to-medium quality content, it can harm high-quality content by forcing exposure to suboptimal audiences, polluting engagement signals and downgrading future recommendation. We recast content promotion as a dual-objective optimization that balances short-term value acquisition with long-term model improvement. To make this tractable at bid time in content promotion, we introduce a decomposable surrogate objective, gradient coverage, and establish its formal connection to Fisher Information and optimal experimental design. We design a two-stage auto-bidding algorithm based on Lagrange duality that dynamically paces budget through a shadow price and optimizes impression-level bids using per-impression marginal utilities. To address missing labels at bid time, we propose a confidence-gated gradient heuristic, paired with a zeroth-order variant for black-box models that reliably estimates learning signals in real time. We provide theoretical guarantees, proving monotone submodularity of the composite objective, sublinear regret in online auction, and budget feasibility. Extensive offline experiments on synthetic and real-world datasets validate the framework: it outperforms baselines, achieves superior final AUC/LogLoss, adheres closely to budget targets, and remains effective when gradients are approximated zeroth-order. These results show that strategic, information-aware promotion can improve long-term model performance and organic outcomes beyond naive impression-maximization strategies.