DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915634568232960 |
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| author | Roychowdhury, Sohini Holeman, Adam Amin, Mohammad Wei, Feng Mehta, Bhaskar Reddy, Srihari |
| author_facet | Roychowdhury, Sohini Holeman, Adam Amin, Mohammad Wei, Feng Mehta, Bhaskar Reddy, Srihari |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations Roychowdhury, Sohini Holeman, Adam Amin, Mohammad Wei, Feng Mehta, Bhaskar Reddy, Srihari Machine Learning Software Engineering 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. |
| title | DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations |
| topic | Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2511.18331 |