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.

Indonesian healthcare superapp Alodokter provides end-to-end digital solutions to patients including telemedicine, doctor bookings, medical content, and health-insurance services. It has more than 28 million monthly active users, and more than 40,000 certified doctors on the platform. Perhaps unsurprisingly, Alodokter found that engagement was high when users were unwell, but that it was difficult to keep people active on the app otherwise. It also found that it had a retention problem, with a lot of uninstalls happening almost immediately after installation. Alodokter's marketing goals were three-fold: increase app engagement to reduce churn and boost retention, increase active users across the app, and improve conversion and clickthrough rates (CVRs and CTRs) of push campaigns to uplift engagement.