DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations

• URL: http://arxiv.org/abs/2511.18331v1
• Date: Sun, 23 Nov 2025 08:10:33 GMT
• Title: DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations
• Authors: Sohini Roychowdhury, Adam Holeman, Mohammad Amin, Feng Wei, Bhaskar Mehta, Srihari Reddy,
• Abstract summary: We introduce Dynamix, a scalable, personalized sequence exploration framework for online ad-recommendation systems.<n>Dynamix categorizes users-engagements at session and surface-levels by leveraging correlations between dwell-times and ad-conversion events.<n>We show that Dynamix achieves significant cost efficiency and performance improvements in online user-sequence based recommendation models.
• Score: 5.168870928194366
• License: http://creativecommons.org/licenses/by/4.0/
• 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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