Advertising Data Characteristics and Sparse Large‑Model Practices at iQIYI

iQIYI’s ad ranking system replaces static, hash‑based embeddings with TFRA dynamic embeddings to efficiently handle massive sparse ID features, eliminates collisions and I/O bottlenecks, isolates memory during hot model swaps, enabling billion‑parameter models that boost revenue by 4.3 % while planning adaptive embedding sizes for future improvements.

AI recommendationAdvertisingSparse Embedding

0 likes · 10 min read

Advertising Data Characteristics and Sparse Large‑Model Practices at iQIYI

DataFunTalk

Apr 3, 2023 · Artificial Intelligence

Large‑Scale Recommendation System Training with TorchRec and Dynamic Embedding

This article explains how Tencent’s AI team leverages the PyTorch‑based TorchRec library and a custom dynamic embedding solution to train billion‑scale recommendation models efficiently, detailing the benefits of TorchRec, GPU embedding, optimized kernels, embedding partition strategies, experimental results, and practical deployment guidance.

GPU EmbeddingLarge-Scale TrainingPyTorch

0 likes · 15 min read

Large‑Scale Recommendation System Training with TorchRec and Dynamic Embedding

Alibaba Cloud Big Data AI Platform

Dec 15, 2022 · Artificial Intelligence

Vivo’s DeepRec: Dynamic Embedding and GPU Tricks that Raised CTR by 1.2%

Vivo’s AI recommendation team leveraged Alibaba’s DeepRec engine—introducing dynamic Embedding Variables, feature admission/elimination, Parquet datasets, and advanced CPU/GPU inference optimizations such as SessionGroup, device placement, multi‑stream and BladeDISC compilation—resulting in notable gains in model accuracy, latency reduction, and resource efficiency.

DeepRecGPU inferenceRecommendation Systems

0 likes · 13 min read

Vivo’s DeepRec: Dynamic Embedding and GPU Tricks that Raised CTR by 1.2%