Selective Test-Time Compute Scaling for Click-Through Rate Prediction via Uncertainty-Triggered Feature Path Exploration
arXiv:2605.24989v1 Announce Type: new Abstract: Scaling test-time compute has proven highly effective for language models, yet this opportunity remains largely unexplored for industrial Click-Through Rate (CTR) prediction. CTR models suffer from a fundamental asymmetry: feature combinations well-represented in training yield confident predictions, while sparsely observed ones produce unreliable outputs. Existing training-phase solutions such as adaptive gating learn a fixed selection function subject to the same sparsity, offering no per-instance recourse at deployment.We propose UTTSI (Uncert
The paper addresses a critical, yet underexplored, area of industrial AI optimization for CTR prediction, building on lessons learned from large language models.
Improved CTR prediction directly translates to more efficient online advertising, higher revenue for platforms, and better user experience through more relevant content.
This research could lead to more dynamic and adaptive CTR models that perform better with sparse data, moving beyond static, predefined model structures.
- · Online advertising platforms
- · E-commerce companies
- · AI/ML researchers in industry
- · Consumers (through better personalization)
- · Platforms with less advanced AI infrastructure
- · Inefficient advertising models
More accurate and cost-effective Click-Through Rate predictions will improve the ROI of digital advertising campaigns.
Increased ad revenue for major online platforms could drive further investment in AI research and infrastructure.
Enhanced personalization capabilities may deepen user engagement on platforms, potentially increasing their market dominance and data harvesting capabilities.
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Read at arXiv cs.LG