Official hub of Tencent Advertising Technology, sharing the team's latest cutting-edge achievements and advertising technology applications.
MimicMotion is a controllable video generation framework that produces smooth, high-quality human motion videos by leveraging skeletal action guidance, addressing challenges in video generation such as limited length, weak controllability, and lack of dynamic detail.
This paper investigates the embedding dimensional collapse problem that hinders scaling of recommendation models and proposes a Multi-Embedding paradigm that learns multiple embeddings per feature with independent expert networks, demonstrating consistent performance gains across major CTR benchmarks and real‑world ad systems.
This article explains how the scaling law—massive data, compute, and a simple transformer architecture—drives generative AI breakthroughs, how Tencent applied this principle to build larger ad models and the "Hunyuan" large model, and how advertising systems must evolve to truly understand content and users.
This article summarizes Tencent Advertising's recent research on recommendation models, covering comprehensive feature encoding techniques, solutions to embedding dimensional collapse through multi‑embedding paradigms, and novel methods such as STEM and AME to disentangle conflicting user interests across multiple tasks.
Tencent’s Taiji machine learning platform, a cloud‑native, distributed parameter‑server system, provides end‑to‑end MLOps for advertising by integrating data ingestion, feature engineering, model training, evaluation, deployment, and monitoring, supporting massive models up to billions of parameters while improving efficiency, scalability, and resource management.
This article discusses experimental design challenges in two-sided markets, particularly in advertising scenarios, and presents various methods including four-table experiments, counterfactual interleaving, and contingency table joint sampling to address issues like network effects and competition between supply and demand sides.
This article presents Tencent's latest advancements in large‑scale model training, detailing the AngelPTM framework and its ZeRO‑Cache optimization techniques that reduce memory and storage costs, improve hardware utilization, and achieve high‑performance training for trillion‑parameter AI models across various applications.
This article details Tencent's development of the 1‑trillion‑parameter HunYuan‑NLP model, covering its MoE architecture, cost‑effective pre‑training strategies, distributed training framework, model compression toolkit, and successful deployment across advertising, gaming, and other Tencent services.
The article details how Tencent's Taiji machine‑learning platform reduces training costs and improves efficiency for large‑scale advertising models by leveraging cloud‑native mixed‑resource strategies—including online idle, offline elastic, and compute‑resource sharing—while maintaining high service stability through advanced scheduling, fault‑tolerance, and resource‑prediction techniques.
The article details how Tencent Advertising re‑architected its massive log pipeline by consolidating heterogeneous real‑time and offline logs into an Iceberg‑based data lake, introducing multi‑level partitioning, Spark and Flink ingestion, and numerous performance and cost optimizations for scalable big‑data analytics.
