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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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Tencent Advertising Technology
Tencent Advertising Technology
Jun 15, 2020 · Artificial Intelligence

Insights from a Top Contestant on the Tencent Advertising Algorithm Competition: Transformer Modeling and Model Fusion

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.

machine learning
0 likes · 4 min read
Insights from a Top Contestant on the Tencent Advertising Algorithm Competition: Transformer Modeling and Model Fusion
Tencent Advertising Technology
Tencent Advertising Technology
Jun 10, 2020 · Artificial Intelligence

Improving Advertising Inventory Forecasting with Deep Spatial‑Temporal Tensor Factorization

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.

AIAdvertisingDeep Learning
0 likes · 11 min read
Improving Advertising Inventory Forecasting with Deep Spatial‑Temporal Tensor Factorization
Tencent Advertising Technology
Tencent Advertising Technology
May 12, 2020 · Artificial Intelligence

Insights and Solution Approaches for the 2020 Tencent Advertising Algorithm Competition

The 2020 Tencent Advertising Algorithm Competition challenges participants to predict user gender and age from 90‑day click logs, and the champion shares data understanding, feature engineering techniques such as one‑hot, TF‑IDF, Word2Vec, and modeling strategies including GBDT and RNN/LSTM/GRU to guide competitors.

AdvertisingCompetitionTencent
0 likes · 4 min read
Insights and Solution Approaches for the 2020 Tencent Advertising Algorithm Competition
Tencent Advertising Technology
Tencent Advertising Technology
May 5, 2020 · Artificial Intelligence

How to Use the TI-ONE SDK to Train Models for the 2020 Tencent Advertising Algorithm Competition

This tutorial walks you through the complete process of using the TI-ONE SDK—including data preparation, dependency installation, session initialization, TensorFlow estimator configuration, job submission, and result monitoring—to train a machine‑learning model for the 2020 Tencent Advertising Algorithm Competition.

SDKTI-ONETencent
0 likes · 7 min read
How to Use the TI-ONE SDK to Train Models for the 2020 Tencent Advertising Algorithm Competition
Tencent Advertising Technology
Tencent Advertising Technology
May 2, 2020 · Artificial Intelligence

How to Use TI-ONE Built‑in Operators for the 2020 Tencent Advertising Algorithm Competition

This tutorial walks you through creating a TI‑ONE project, ingesting competition data, configuring and training a decision‑tree model with built‑in operators, running the workflow, and downloading and uploading the result files for the 2020 Tencent Advertising Algorithm Competition.

Data PipelineModel TrainingTI-ONE
0 likes · 7 min read
How to Use TI-ONE Built‑in Operators for the 2020 Tencent Advertising Algorithm Competition
Tencent Advertising Technology
Tencent Advertising Technology
Feb 28, 2020 · Artificial Intelligence

Bayesian Smoothing and Key-Value Memory Networks for Click-Through Rate Prediction in Recommendation Systems

This article presents a Bayesian smoothing approach to alleviate cold-start problems in click-through rate estimation, introduces key-value memory networks to incorporate prior knowledge, and proposes methods to convert continuous features into dictionary embeddings for deep learning models in recommendation systems.

Deep Learningclick-through ratecontinuous feature embedding
0 likes · 18 min read
Bayesian Smoothing and Key-Value Memory Networks for Click-Through Rate Prediction in Recommendation Systems