DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations
Sohini Roychowdhury, Adam Holeman, Mohammad Amin, Feng Wei, Bhaskar Mehta, Srihari Reddy

TL;DR
Dynamix is a scalable framework that enhances personalized ad-recommendation efficiency by selectively exploring user engagement histories using self-supervised learning, achieving cost and performance improvements.
Contribution
It introduces a novel dynamic resource exploration method that optimizes user engagement processing with self-supervised event features, improving efficiency without sacrificing accuracy.
Findings
Training throughput increased by 1.15%.
Inference throughput increased by 1.8%.
Inference QPS boosted by 4.2%.
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…
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Taxonomy
TopicsRecommender Systems and Techniques · Advanced Graph Neural Networks · Explainable Artificial Intelligence (XAI)
