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arXiv · 2511.18331

DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations

Sohini Roychowdhury·Adam Holeman·Mohammad Amin·Feng Wei·Bhaskar Mehta·Srihari Reddy
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Abstract

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.

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Sohini Roychowdhury, Adam Holeman, Mohammad Amin, Feng Wei, Bhaskar Mehta, Srihari Reddy. 2025-11-23. DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations. https://arxiv.org/abs/2511.18331

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