MATT-CTR: Unleashing a Model-Agnostic Test-Time Paradigm for CTR Prediction with Confidence-Guided Inference Paths
arXiv:2510.08932v2 Announce Type: replace Abstract: Recently, a growing body of research has focused on either optimizing CTR model architectures to better model feature interactions or refining training objectives to aid parameter learning, thereby achieving better predictive performance. However, previous efforts have primarily focused on the training phase, largely neglecting opportunities for optimization during the inference phase. Infrequently occurring feature combinations, in particular, can degrade prediction performance, leading to unreliable or low-confidence outputs. To unlock the
The increasing complexity of AI models and the critical need for reliable predictions in real-world applications drive the imperative for optimizing inference performance and addressing infrequent feature combinations.
Improving CTR prediction during inference directly impacts the efficiency and accuracy of ad placement, recommender systems, and user engagement, which are core components of digital economies.
The focus expands from solely training optimization to a holistic approach that includes optimizing the inference phase, particularly for handling edge cases and improving prediction confidence.
- · Ad platforms
- · E-commerce companies
- · AI model developers
- · Computational infrastructure providers
- · Companies with inefficient inference pipelines
- · Legacy recommendation systems
More accurate and reliable CTR predictions lead to improved monetization for digital platforms.
Enhanced prediction reliability can accelerate the adoption of AI-driven decision-making in sensitive applications beyond traditional ad tech.
Increased efficiency in inference could reduce the computational overhead and energy footprint of large-scale AI deployments, influencing the broader compute supply chain.
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Read at arXiv cs.LG