Official hub of Tencent Advertising Technology, sharing the team's latest cutting-edge achievements and advertising technology applications.
This article announces a live broadcast on federated learning for advertising and recommendation, featuring experts from Tencent Advertising and WeBank, discussing how federated learning enables privacy-preserving data sharing to enhance marketing effectiveness.
The article details the Tencent Advertising Algorithm Competition semifinal, presenting the Embedding team's pure Transformer model with joint ID word embeddings, data preprocessing, training hyperparameters, model architecture, and practical tips for improving age/gender prediction from click sequences.
This article details the 2020 Tencent Advertising Algorithm Competition, describing the user profiling task, data fields, feature engineering, Python code for ID mapping and Word2Vec training, multiple model architectures (LSTM, CNN-Inception, transformer), and the final performance results achieved by the team.
The article details the Tencent Advertising Algorithm competition final, explains the chizhu team's approach of converting ad IDs into word sequences for text classification using large‑scale word2vec embeddings and a dual BiLSTM architecture, presents custom loss functions, training tricks, and shares full Python model code, achieving an overall rank of 11.
In this interview, DYG team member Wang He introduces his teammates, explains why they formed the team, and shares detailed model‑fusion techniques—including input variation, diverse model architectures, and training‑target differences—to boost competition scores during the final stage.
The Tencent Advertising Rhinoceros Bird Special Research Program, launched in June 2020, invites global academia to collaborate on advertising technology challenges in AI, big data, and related fields, outlining the application process, evaluation criteria, and accompanying Wiztalk lecture series.
The article explains how the GEAR model uses graph neural networks and BERT representations to aggregate multiple pieces of evidence for fact verification, improving accuracy on datasets like FEVER and offering applications in misinformation detection, knowledge‑graph completion, and advertising analytics.
In this article, a second‑place contestant from Xiamen University shares his practical experience with word2vec‑based sequence models, transformer learning‑rate tuning, handling masked positions in max‑pooling, and techniques for increasing model diversity through input and parameter variations for a large‑scale advertising algorithm competition.
The article explains how advertising inventory forecasting—predicting how many users will see a specific ad—poses challenges due to fluctuating traffic and user segmentation, and describes a new deep spatial‑temporal tensor factorization model that dramatically improves prediction accuracy, scalability, and robustness for large‑scale ad platforms.
This article shares a competition winner's step‑by‑step solution for predicting user age and gender from ad click sequences, treating IDs as words, using word2vec embeddings, a custom transformer‑LSTM model, dual‑task loss, and weight‑search post‑processing.
