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Oct 27, 2025 · Artificial Intelligence

How Large Language Models Are Revolutionizing Generative Recommendation Systems

Over the past year, generative recommendation has made substantial progress by leveraging large language models' powerful sequence modeling and reasoning abilities, introducing a new paradigm that replaces complex handcrafted features, addresses traditional recommendation bottlenecks, and outlines the evolution, core technologies, engineering challenges, and future directions of LLM‑based recommendation systems.

AI EngineeringEncoder-DecoderLLM

0 likes · 29 min read

How Large Language Models Are Revolutionizing Generative Recommendation Systems

Ximalaya Technology Team

Oct 9, 2023 · Artificial Intelligence

DeepRec-Based High-Dimensional Sparse Feature Support and Real-Time Model Training in Ximalaya AI Cloud

Ximalaya AI Cloud leverages DeepRec’s Embedding Variable to elastically manage high‑dimensional sparse features with low collision, supporting admission/eviction, multi‑level storage and minute‑level incremental model updates, which together boost GPU utilization, halve training time and improve recommendation CTR by 2‑3 % while maintaining latency.

AI cloudDeepRecKubernetes

0 likes · 13 min read

DeepRec-Based High-Dimensional Sparse Feature Support and Real-Time Model Training in Ximalaya AI Cloud

AntTech

Jul 12, 2023 · Artificial Intelligence

Hybrid Embedding Architecture for Large‑Scale Sparse CTR Models

This article describes the Hybrid Embedding solution proposed by Ant AI Infra to address storage, resource, and feature‑governance challenges of massive sparse CTR models, detailing its multi‑layer storage design, KV‑based parameter server, and performance gains in large‑scale recommendation systems.

AI InfraCTRHybrid Embedding

0 likes · 9 min read

Hybrid Embedding Architecture for Large‑Scale Sparse CTR Models

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%

360 Zhihui Cloud Developer

Sep 16, 2020 · Artificial Intelligence

How TensorNet Supercharges Sparse Feature Training on TensorFlow

TensorNet is a TensorFlow‑based distributed training framework optimized for massive sparse‑feature models in advertising and recommendation, dramatically reducing parameter sync overhead, enabling near‑infinite feature dimensions, cutting training time from hours to minutes, and boosting inference performance by up to 35%.

AIRecommendation SystemsTensorFlow

0 likes · 10 min read

How TensorNet Supercharges Sparse Feature Training on TensorFlow

360 Tech Engineering

Sep 14, 2020 · Artificial Intelligence

TensorNet: A Distributed Training Framework Optimized for Large-Scale Sparse Feature Models on TensorFlow

TensorNet is a TensorFlow‑based distributed training framework that tackles the challenges of massive data and billions of sparse parameters in advertising and recommendation systems by enabling near‑infinite sparse feature dimensions, drastically reducing synchronization overhead, and delivering up to 35% inference speed improvements.

AI InfrastructureDistributed TrainingTensorFlow

0 likes · 8 min read

TensorNet: A Distributed Training Framework Optimized for Large-Scale Sparse Feature Models on TensorFlow

DataFunTalk

May 15, 2020 · Artificial Intelligence

Optimizing Sparse Feature Embedding for Large‑Scale Recommendation and CTR Prediction

The article reviews recent research on representing massive sparse features in click‑through‑rate (CTR) models, introducing Alibaba's Res‑embedding method and Google's Neural Input Search (NIS) approach, and discusses how these techniques improve embedding efficiency and model generalization in large‑scale recommendation systems.

CTR predictionDeep LearningRecommendation Systems

0 likes · 10 min read

Optimizing Sparse Feature Embedding for Large‑Scale Recommendation and CTR Prediction

DataFunTalk

Apr 12, 2020 · Artificial Intelligence

Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems

In this article, Wang Zhe addresses fifteen common questions about recommendation systems, covering topics such as building cross‑domain knowledge, the role of deep reinforcement learning, handling sparse or low‑sample data, offline‑online evaluation, knowledge graphs, graph neural networks, model interpretability, large‑scale ID embedding, and career advice for engineers.

Deep LearningGraph Neural NetworkKnowledge Graph

0 likes · 14 min read

Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems