The expert live Q&A session for Tencent's Social Ads algorithm competition was recorded and the full FAQ, covering data labeling, feature handling, model selection, imbalance mitigation, and practical engineering tips, is presented here for participants and researchers alike.
This article compares click‑through rate (CTR) prediction and conversion rate (CVR) prediction in the context of Tencent’s social advertising competition, highlighting their shared binary‑classification nature while detailing key differences in data collection, feedback latency, product‑type bias, definition richness, and data sparsity.
This article examines the challenges of high‑dimensional sparse features in search ranking, explains why traditional linear models struggle, and describes how deep neural networks with novel encoding schemes and online updates can dramatically improve CTR prediction and real‑time performance.
This article presents how deep learning techniques are applied to Sogou's mobile search advertising, detailing the system architecture, feature design, multi‑model fusion strategies, engineering implementation, evaluation metrics, and future directions for improving CTR prediction performance.
This article explains the principles of Factorization Machines (FM) and Field‑aware Factorization Machines (FFM), their implementation details, and how Meituan‑Dianping applied FFM in a DSP platform to achieve superior CTR and CVR estimation for sparse, high‑dimensional advertising data.
This article presents a comprehensive overview of ranking learning techniques used in Mobile Taobao's recommendation system, covering problem definition, pointwise/pairwise/listwise approaches, feature engineering, online learning, industry applications, and future optimization strategies.
Baidu's Big Data Machine Learning team, led by Xia Fen, unveiled a suite of five novel algorithms that together allow trillion‑scale feature processing, minute‑level model updates, and up to thousand‑fold efficiency gains in training and inference, dramatically surpassing existing solutions such as Google's billion‑feature systems.
