动态优化广告推荐中的用户行为数据处理，提升效率与精度。
DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations
- 基于事件特征和自监督学习，动态筛选用户行为数据。
- 训练吞吐提升1.15%，推理吞吐提升1.8%，精度提升0.033 NE。
- 适合大规模在线推荐系统，尤其关注计算效率的场景。
在线广告推荐系统处理完整的用户-广告-互动历史既计算密集又易受噪声干扰。本文提出Dynamix，一种可扩展的个性化序列探索框架，通过最大相关性原则与基于事件特征（EBFs）的自监督学习优化事件历史处理。Dynamix利用停留时长与广告转化事件间的相关性，在会话和表面层级对用户互动进行分类，实现针对特定用户群体的事件级特征剔除与选择性特征增强。该方法在不牺牲广告点击预测准确率的前提下，显著提升训练与推理效率。实验表明，动态资源移除使训练和推理吞吐分别提升1.15%和1.8%；动态特征增强在保持基线模型性能的同时，带来0.033 NE的精度提升，并使推理QPS提升4.2%。结果证明，Dynamix在基于用户序列的推荐模型中实现了显著的成本效益与性能提升。自监督用户分组与资源探索可进一步优化复杂特征选择策略，兼顾工作流与计算资源。
原文摘要 · Abstract (English)
For online ad-recommendation systems, processing complete user-ad-engagement histories is both computationally intensive and noise-prone. We introduce Dynamix, a scalable, personalized sequence exploration framework that optimizes event history processing using maximum relevance principles and self-supervised learning through Event Based Features (EBFs). Dynamix categorizes users-engagements at session and surface-levels by leveraging correlations between dwell-times and ad-conversion events. This enables targeted, event-level feature removal and selective feature boosting for certain user-segments, thereby yielding training and inference efficiency wins without sacrificing engaging ad-prediction accuracy. While, dynamic resource removal increases training and inference throughput by 1.15% and 1.8%, respectively, dynamic feature boosting provides 0.033 NE gains while boosting inference QPS by 4.2% over baseline models. These results demonstrate that Dynamix achieves significant cost efficiency and performance improvements in online user-sequence based recommendation models. Self-supervised user-segmentation and resource exploration can further boost complex feature selection strategies while optimizing for workflow and compute resources.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。