AI 中文总结
研究在个性化特征受限下的广告推荐问题，提出RAMP方法，通过个性化路径、非个性化路径及预测对齐架构，提升CTR/CVR预测准确性，实验表明该方法在个性化特征缺失时性能优于现有方法，全特征可用时也具竞争力。
AI 中文摘要
点击率（CTR）和转化率（CVR）预测是在线广告中的基本任务，旨在基于各种特征估计用户交互的可能性。尽管年龄和性别等个性化属性可显著提高预测准确性，但受隐私法规限制其使用，限制了训练和推理可用数据。为应对这一挑战，我们提出RAMP，在无法访问个性化特征时提高CTR/CVR预测准确性。它由基于双塔组件的个性化路径、仅使用非个性化特征训练的单独非个性化路径以及二者间的预测对齐架构组成。我们使用公共基准和工业数据集进行综合实验评估其性能。结果表明，当缺少个性化特征时，RAMP始终优于现有方法，在所有特征可用时也保持有竞争力的性能，证明了其在实际广告系统中的有效性和实用性。
英文摘要
Click-through rate (CTR) and conversion rate (CVR) prediction are fundamental tasks in online advertising, aiming to estimate the likelihood of user interactions based on various features. While personalized attributes such as age and gender can significantly enhance predictive accuracy, their use is increasingly restricted by privacy regulations, thereby limiting available data for both training and inference. To address this challenge, we propose RAMP (Robust Ad Recommendation Under Limited Personalized-Feature Availability via Masking and Alignment Pathways), which is designed to improve CTR/CVR prediction accuracy when personalized features are not accessible, thus supporting deployment in privacy-constrained settings.RAMP consists of (i) a personalized pathway built upon a dual-tower component with identical inputs but independent parameters, where output masking separates predictions for personalized and non-personalized signals, (ii) a separate non-personalized pathway trained with non-personalized features only, and (iii) a distillation-inspired prediction-alignment architecture between (i) and (ii) that improves prediction when personalized features are unavailable. We conduct comprehensive experiments using both public benchmarks and industrial datasets to evaluate the performance of RAMP. Our evaluation spans multiple backbone models and different settings: with and without access to personalized features. The results show that RAMP consistently outperforms state-of-the-art methods when personalized features are missing, while maintaining competitive performance when all features are available. %demonstrating its effectiveness and practicality for real-world advertising systems. Our code is publicly available at https://github.com/Ruixinhua/RAMP.
Comments12 pages, 4 figures, accepted to ICTIR '26
