RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
Hua Zong, Qingtao Zeng, Zhengxiong Zhou, Zhihua Han, Zhensong Yan, Mingjie Liu, Hechen Sun, Jiawei Liu, Yiwen Hu, Qi Wang, YiHan Xian, Wenjie Guo, Houyuan Xiang, Zhiyuan Zeng, Xiangrong Sheng, Bencheng Yan, Nan Hu, Yuheng Huang, Jinqing Lian, Ziru Xu, Yan Zhang, Ju Huang, Siran Yang, Huimin Yi, Jiamang Wang, Pengjie Wang, Han Zhu, Jian Wu, Dan Ou, Jian Xu, Haihong Tang, Yuning Jiang, Bo Zheng, Lin Qu
公開日: 2025/9/25
Abstract
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.