embedding reduction

DataFunTalk

Jun 14, 2021 · Artificial Intelligence

From Massive to Compact: Model Compression Strategies for Large‑Scale CTR Prediction in Alibaba Search Advertising

This article describes how Alibaba's search advertising team transformed trillion‑parameter CTR models into lightweight, high‑precision systems by compressing embedding layers through feature‑space reduction, dimension quantization, and multi‑hash techniques, while also introducing graph‑based pre‑training and dropout‑driven feature selection to maintain accuracy.

CTR predictionGraph Neural Networksembedding reduction

0 likes · 15 min read

From Massive to Compact: Model Compression Strategies for Large‑Scale CTR Prediction in Alibaba Search Advertising

Alimama Tech

Jun 2, 2021 · Artificial Intelligence

Model Compression and Feature Optimization for Large-Scale CTR Prediction in Advertising

Alibaba‑Mama’s advertising team shrank multi‑terabyte CTR models to just tens of gigabytes by applying row‑dimension embedding compression, multi‑hash embeddings, graph‑based relationship networks, PCF‑GNN pre‑training, and droprank feature selection, preserving accuracy while halving training time, doubling online QPS, and retiring hundreds of servers.

CTR predictionGraph Neural NetworksLarge-scale ML

0 likes · 14 min read

Model Compression and Feature Optimization for Large-Scale CTR Prediction in Advertising