Optimization of Deep Learning-Based CTR Models in Advertising
This report presents recent advances in optimizing deep learning click‑through‑rate models for advertising, including improved embedding mechanisms, novel feature‑interaction and architecture designs such as attention‑based behavior sequencing, multi‑tower and Mixture‑of‑Experts networks, dynamic ID handling, hourly updates, incremental training, and outlines future multi‑modal and embedding‑importance research.
This technical report details advancements in optimizing deep learning-based click-through rate (CTR) models for advertising applications. Key improvements include enhanced embedding mechanisms, feature interaction strategies, and model architecture innovations. The study covers sparse and dense component optimizations, dynamic ID feature management, and training framework upgrades. Specific techniques like attention mechanisms for behavioral sequence modeling, multi-tower structures for multi-ad-slot scenarios, and Mixture-of-Experts (MoE) networks for dynamic ad-slot adaptation are discussed. The report also addresses model timeliness improvements through hourly updates and long-term incremental training strategies. Future directions focus on multi-modal content understanding and embedding importance differentiation.
Ximalaya Technology Team
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