{"ID":2837940,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.18331","arxiv_id":"2511.18331","title":"DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.18331","url_pdf":"https://arxiv.org/pdf/2511.18331v1","authors":"[\"Sohini Roychowdhury\",\"Adam Holeman\",\"Mohammad Amin\",\"Feng Wei\",\"Bhaskar Mehta\",\"Srihari Reddy\"]","published":"2025-11-23T08:10:33Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.SE\"]","methods":"[\"LoRA\"]","has_code":false}