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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.00324 |
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| _version_ | 1866910183371833344 |
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| author | Di, Jieming Chen, Xiaoyu She, Ying Wang, Siyu Liu, Lizzie Wu, Fenggang Mu, Jiaoying Tsui, Tony Elroumy, Amr Tang, Hsing Jiang, Zewei Yang, Qiao Qi, Lin Lin, Haibo Cui, Weifeng Li, Daniel Gupta, Kapil Singh, Shivendra Pratap Zheng, Jie Overwijk, Arnold Leng, Ling Reddy, Sri Malkin, Robert Liu, Rocky |
| author_facet | Di, Jieming Chen, Xiaoyu She, Ying Wang, Siyu Liu, Lizzie Wu, Fenggang Mu, Jiaoying Tsui, Tony Elroumy, Amr Tang, Hsing Jiang, Zewei Yang, Qiao Qi, Lin Lin, Haibo Cui, Weifeng Li, Daniel Gupta, Kapil Singh, Shivendra Pratap Zheng, Jie Overwijk, Arnold Leng, Ling Reddy, Sri Malkin, Robert Liu, Rocky |
| contents | Large-scale ranking systems depend on thousands of features derived from user behavior across multiple time horizons. Typically requires model retraining -- resulting in long iteration cycles (3--6 months), substantial GPU resource consumption, and limited rollout throughput.
We introduce Intelligent Elastic Feature Fading (IEFF), a production infrastructure system that enables retrain-free feature efficiency rollouts by elastically controlling feature coverage and distribution at serving time. IEFF supports incremental feature coverage adjustments while models adapt through recurring training, eliminating dependencies on explicit retraining cycles. The system incorporates strict safety guardrails, reversibility mechanisms, and comprehensive monitoring to ensure stability at scale.
Across multiple production use cases, IEFF accelerates efficiency-related rollouts by 5$\times$, eliminates retraining-related GPU overhead, and enables faster capacity recycling. Extensive offline and online experiments demonstrate that gradual feature fading prevents 50--55\% of online performance degradation compared to abrupt feature removal, while maintaining stable model behavior. These results establish elastic, system-level feature fading as a practical and scalable approach for managing feature efficiency in modern industrial ranking systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00324 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Intelligent Elastic Feature Fading: Enabling Model Retrain-Free Feature Efficiency Rollouts at Scale Di, Jieming Chen, Xiaoyu She, Ying Wang, Siyu Liu, Lizzie Wu, Fenggang Mu, Jiaoying Tsui, Tony Elroumy, Amr Tang, Hsing Jiang, Zewei Yang, Qiao Qi, Lin Lin, Haibo Cui, Weifeng Li, Daniel Gupta, Kapil Singh, Shivendra Pratap Zheng, Jie Overwijk, Arnold Leng, Ling Reddy, Sri Malkin, Robert Liu, Rocky Information Retrieval Machine Learning Large-scale ranking systems depend on thousands of features derived from user behavior across multiple time horizons. Typically requires model retraining -- resulting in long iteration cycles (3--6 months), substantial GPU resource consumption, and limited rollout throughput. We introduce Intelligent Elastic Feature Fading (IEFF), a production infrastructure system that enables retrain-free feature efficiency rollouts by elastically controlling feature coverage and distribution at serving time. IEFF supports incremental feature coverage adjustments while models adapt through recurring training, eliminating dependencies on explicit retraining cycles. The system incorporates strict safety guardrails, reversibility mechanisms, and comprehensive monitoring to ensure stability at scale. Across multiple production use cases, IEFF accelerates efficiency-related rollouts by 5$\times$, eliminates retraining-related GPU overhead, and enables faster capacity recycling. Extensive offline and online experiments demonstrate that gradual feature fading prevents 50--55\% of online performance degradation compared to abrupt feature removal, while maintaining stable model behavior. These results establish elastic, system-level feature fading as a practical and scalable approach for managing feature efficiency in modern industrial ranking systems. |
| title | Intelligent Elastic Feature Fading: Enabling Model Retrain-Free Feature Efficiency Rollouts at Scale |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2605.00324 |