How Multi-Attribution Learning Boosts Conversion Rate Prediction in Display Ads

This article introduces Multi-Attribution Learning (MAL), a novel paradigm that jointly models multiple attribution labels to overcome the single-attribution bottleneck in conversion rate (CVR) prediction, detailing its architecture, auxiliary tasks, extensive offline and online experiments, and significant business gains.

advertising systemsconversion rate predictionmulti-attribution learning

0 likes · 24 min read

How Multi-Attribution Learning Boosts Conversion Rate Prediction in Display Ads

JD Tech

Apr 8, 2025 · Artificial Intelligence

MaRCA: Multi‑Agent Reinforcement Learning Computation Allocation for Full‑Chain Advertising Systems

The article presents MaRCA, a multi‑agent reinforcement learning framework that models user value, compute consumption, and action reward to allocate limited computation resources across the entire advertising recommendation pipeline, achieving higher ad revenue while keeping system load stable under fluctuating traffic and diverse request values.

Deep LearningLoad-Aware SchedulingResource Optimization

0 likes · 16 min read

MaRCA: Multi‑Agent Reinforcement Learning Computation Allocation for Full‑Chain Advertising Systems

21CTO

Sep 1, 2021 · Databases

How BaikalDB Redefines Cloud‑Native Distributed Databases for Modern Business Needs

This article examines the evolving data‑storage demands of large‑scale commercial advertising systems, traces the development of BaikalDB from early MySQL sharding to a heterogeneous, cloud‑native distributed database, and explains its storage, compute, and scheduling designs that deliver high reliability, low cost, and millisecond‑level performance.

BaikalDBSQL Compatibilityadvertising systems

0 likes · 27 min read

How BaikalDB Redefines Cloud‑Native Distributed Databases for Modern Business Needs

Baidu Geek Talk

Aug 16, 2021 · Artificial Intelligence

End-to-End Consistency Testing Solution for Click-Through Rate Models in Advertising Systems

The article describes Baidu’s end-to-end consistency testing framework for advertising click-through-rate models, which uses a five-stream verification pipeline and six implementation phases to compare Q-values across feature extraction, table conversions, and DNN computation, enabling precise detection and localization of data and model inconsistencies in production.

BaiduCTR predictionMachine learning testing

0 likes · 17 min read

End-to-End Consistency Testing Solution for Click-Through Rate Models in Advertising Systems

DataFunTalk

Aug 10, 2021 · Artificial Intelligence

A Comprehensive Review of Industrial-Scale Deep Learning for Click-Through Rate Prediction in Online Advertising

This article provides an extensive retrospective and forward‑looking analysis of the evolution of click‑through‑rate prediction technologies in online advertising, covering shallow‑learning era challenges, the rise of industrial‑scale deep learning, system‑level innovations such as recall, coarse‑ranking, fine‑ranking, bidding, and the emerging co‑design of algorithms, compute, and architecture.

Algorithmic OptimizationCTR predictionCompute Efficiency

0 likes · 65 min read

A Comprehensive Review of Industrial-Scale Deep Learning for Click-Through Rate Prediction in Online Advertising