2 papers
cs.DC2026
RecIS: Unifying Sparse and Dense Computation for Efficient Recommendation Training
Hua Zong, Shuai Yang, Zhengxiong Zhou +14
Modern recommender models scale sparse representations and dense computation together. They process thousands of heterogeneous feature fields, long user-behavior sequences, and tri…
cs.IR2025
RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
Hua Zong, Qingtao Zeng, Zhengxiong Zhou +31
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 fra…