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Tencent Advertising Technology

Official hub of Tencent Advertising Technology, sharing the team's latest cutting-edge achievements and advertising technology applications.

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Latest from Tencent Advertising Technology

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Tencent Advertising Technology

Dec 27, 2024 · Artificial Intelligence

Tencent's AutoML Research for Advertising Recommendation Systems

This article outlines Tencent's AutoML research, presenting several recent papers that introduce novel neural architecture search, feature selection, pooling, embedding size, and hyper‑parameter optimization techniques to improve the efficiency, accuracy, and scalability of large‑scale advertising recommendation systems.

AutoMLEmbedding Size SearchNeural Architecture Search

0 likes · 10 min read

Tencent's AutoML Research for Advertising Recommendation Systems

Tencent Advertising Technology

Dec 6, 2024 · Big Data

Building a High‑Performance Advertising Feature Data Lake with Apache Iceberg at Tencent

Tencent's advertising team replaced a traditional HDFS‑Hive warehouse with an Apache Iceberg‑based data lake, adding primary‑key tables, multi‑stream merging, adaptive compaction, and Spark SPJ optimizations to achieve minute‑level feature update latency, 10× back‑fill speed, and up to 60% storage savings.

CDCCompactionData Lake

0 likes · 25 min read

Building a High‑Performance Advertising Feature Data Lake with Apache Iceberg at Tencent

Tencent Advertising Technology

Nov 8, 2024 · Artificial Intelligence

Optimizing Real-Time Bidding: Machine Learning Approaches for Bid Shading and Winning Price Prediction

This article explores advanced machine learning techniques for optimizing bid shading in real-time advertising auctions, introducing a mixed censorship multi-task learning framework and a cost-effective active learning strategy to accurately predict winning price distributions and overcome sample selection bias.

Active LearningAuction MechanismsBid Shading

0 likes · 16 min read

Optimizing Real-Time Bidding: Machine Learning Approaches for Bid Shading and Winning Price Prediction

Tencent Advertising Technology

Oct 23, 2024 · Artificial Intelligence

FedMix: Boosting Vertical Federated Learning with Data Mixture

This paper introduces FedMix, a method that enhances vertical federated learning by mixing aligned and unaligned data, theoretically demonstrating the value of unaligned data and empirically achieving over 10% ROI improvement and significant AUC gains while keeping computational and communication overhead low.

data mixturefederated learningprivacy

0 likes · 11 min read

FedMix: Boosting Vertical Federated Learning with Data Mixture

Tencent Advertising Technology

Oct 17, 2024 · Artificial Intelligence

Long Sequence Modeling for Advertising Recommendation: TIN, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec

This article presents a comprehensive solution for heterogeneous long‑behavior sequence modeling in advertising recommendation, introducing the TIN backbone, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec, along with platform‑level optimizations that enable million‑scale sequences while delivering significant online performance gains.

AdvertisingDeep LearningRecommendation

0 likes · 15 min read

Long Sequence Modeling for Advertising Recommendation: TIN, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec

Tencent Advertising Technology

Oct 14, 2024 · Artificial Intelligence

Generative Retrieval Based on Yuan Large Model: Implementation and Practice in Tencent Advertising

This paper presents the implementation and practice of generative retrieval based on Yuan large model in Tencent Advertising, addressing three key challenges: user intent capture, model alignment in advertising domain, and high-performance platform design under ROI constraints.

Model Optimizationadvertising technologygenerative retrieval

0 likes · 17 min read

Generative Retrieval Based on Yuan Large Model: Implementation and Practice in Tencent Advertising

Tencent Advertising Technology

Aug 27, 2024 · Artificial Intelligence

Auxiliary Ranking Loss Enhances Classification Ability in Sparse‑Feedback CTR Prediction

This study investigates how adding an auxiliary ranking loss to click‑through‑rate (CTR) models not only improves ranking but also alleviates gradient‑vanishing for negative samples, thereby boosting the primary classification performance, especially under sparse positive‑feedback conditions.

AdvertisingCTR predictionGradient Analysis

0 likes · 13 min read

Auxiliary Ranking Loss Enhances Classification Ability in Sparse‑Feedback CTR Prediction

Tencent Advertising Technology

Aug 26, 2024 · Artificial Intelligence

ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising

ADSNet introduces an adaptive Siamese network for cross‑domain lifetime value (LTV) prediction in advertising, leveraging external channel data to mitigate sample sparsity, employing gain‑based sample selection, domain adaptation, and ordinal classification to improve pLTV accuracy and address negative transfer.

AdvertisingDomain AdaptationLTV prediction

0 likes · 12 min read

ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising

Tencent Advertising Technology

Aug 15, 2024 · Artificial Intelligence

Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding

This paper introduces RLLR, a label‑sensitive reward reinforcement learning method that improves natural language understanding tasks by aligning training objectives with label accuracy, and demonstrates its effectiveness across eight public NLU datasets and real‑world advertising feature evaluation, outperforming standard RLHF and SFT baselines.

AdvertisingNatural Language UnderstandingRLHF

0 likes · 14 min read

Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding

Tencent Advertising Technology

Aug 13, 2024 · Artificial Intelligence

Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors

This paper investigates selection bias in large language models for multiple‑choice tasks, proposes metrics to quantify symbol‑content binding, introduces Reweighting Symbol‑Content Binding (RSCB) and Point‑wise Intelligent Feedback (PIF) methods, and demonstrates their effectiveness in reducing bias and improving accuracy, including a real‑world Tencent advertising feature‑evaluation deployment.

Reinforcement LearningSymbol Bindingmultiple choice

0 likes · 16 min read

Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors