RecIS统一稀疏与稠密训练，提升推荐系统效率。
RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
- 基于PyTorch构建统一框架，支持大模型增强的推荐训练。
- 稀疏模块效率优于传统TensorFlow方案，稠密部分复用PyTorch优化技术。
- 已在阿里大规模应用，兼容传统稀疏模型和大模型推荐任务。
本文提出RecIS，一种基于PyTorch生态的统一稀疏-稠密训练框架，旨在满足工业级推荐模型与大模型融合的训练需求。该框架实现两大目标：一是构建统一训练架构，支持大模型增强的推荐系统训练；二是优化稀疏组件，相比基于TensorFlow的推荐模型具有更高效率，稠密部分则利用PyTorch现有优化技术。目前，RecIS已在阿里巴巴用于多个大模型增强的推荐训练任务，部分传统稀疏模型也已开始在该框架下训练。
原文摘要 · Abstract (English)
In this paper, we propose RecIS, a unified Sparse-Dense training framework designed to achieve two primary goals: 1. Unified Framework To create a Unified sparse-dense training framework based on the PyTorch ecosystem that meets the training needs of industrial-grade recommendation models that integrated with large models. 2.System Optimization To optimize the sparse component, offering superior efficiency over the TensorFlow-based recommendation models. The dense component, meanwhile, leverages existing optimization technologies within the PyTorch ecosystem. Currently, RecIS is being used in Alibaba for numerous large-model enhanced recommendation training tasks, and some traditional sparse models have also begun training in it.
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