DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations

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 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.