Official hub of Tencent Advertising Technology, sharing the team's latest cutting-edge achievements and advertising technology applications.
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.
The 2020 Tencent Advertising Algorithm Contest has launched a series of live training sessions to help participants master the TI-ONE and Angel platforms, featuring expert demonstrations on May 15th.
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.
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.
This tutorial walks through creating a TI-ONE Notebook instance, importing competition data, training a TensorFlow model, and uploading results, providing a complete end‑to‑end workflow for participants of the 2020 Tencent Advertising Algorithm Competition.
This tutorial walks participants through using Tencent Cloud's TI-ONE machine learning platform to set up projects, workflows, data paths, train models with built-in frameworks like TensorFlow, and submit results 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.
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.
The talk outlines how Tencent Advertising leverages deep‑learning visual algorithms—including GCN‑based edge refinement, template generation, AutoML‑driven smart review, and a dual‑tower click‑through‑rate model—to automate creative production, improve ad quality, and enhance user experience across creation, review, and playback stages.
The article details a contestant's end‑to‑end approach for an advertising competition, covering data analysis, rule‑based preprocessing, a three‑layer neural network architecture, model‑rule ensemble weighting, self‑correction strategies for the B phase, and final model‑only solutions that achieved top scores.
