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 predictive potential of trained CTR models, we propose a Model-Agnostic Test-Time paradigm (MATT), which leverages the confidence scores of feature combinations to guide the generation of multiple inference paths, thereby mitigating the influence of low-confidence features on the final prediction. Specifically, to quantify the confidence of feature combinations, we introduce a hierarchical probabilistic hashing method to estimate the occurrence frequencies of feature combinations at various orders, which serve as their corresponding confidence scores. Then, using the confidence scores as sampling probabilities, we generate multiple instance-specific inference paths through iterative sampling and subsequently aggregate the prediction scores from multiple paths to conduct robust predictions. Finally, extensive offline experiments and online A/B tests strongly validate the compatibility and effectiveness of MATT across existing CTR models.

翻译：近年来，越来越多的研究聚焦于优化CTR模型架构以更好地建模特征交互，或改进训练目标以辅助参数学习，从而提升预测性能。然而，先前工作主要集中于训练阶段，很大程度上忽略了推理阶段的优化机会。特别是那些出现频率较低的特征组合，往往会降低预测性能，导致输出结果不可靠或置信度较低。为充分释放已训练CTR模型的预测潜力，本文提出一种模型无关的测试时范式（MATT），该范式利用特征组合的置信度分数来引导生成多条推理路径，从而减轻低置信度特征对最终预测的影响。具体而言，为量化特征组合的置信度，我们引入一种分层概率哈希方法来估计不同阶数特征组合的出现频率，并将其作为相应的置信度分数。随后，以置信度分数作为采样概率，通过迭代采样生成多条实例特定的推理路径，进而聚合多条路径的预测分数以进行鲁棒预测。最后，大量离线实验与在线A/B测试充分验证了MATT在现有CTR模型中的兼容性与有效性。