Request-Only Optimization for Recommendation Systems

Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval 2026 Open access

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Authors: Liang Guo, Wei Li, Lucy Liao, Huihui Cheng, Rui Zhang, Yu Shi, Yueming Wang, Yanzun Huang, Keke Zhai, Pengchao Wang, Timothy Shi, Xuan Cao
+16 more Shengzhi Wang, Renqin Cai, Zhaojie Gong, Omkar Vichare, Rui Jian, Leon Gao, Shiyan Deng, Xingyu Liu, Xiong Zhang, Fu Li, Wenlei Xie, Bin Wen, Rui Li, Lu Fang, Xing Liu, Jiaqi Zhai
DOI: 10.1145/3805712.3808469
Cited by: 1
References: 24

Abstract

Recommendation systems represent one of the largest machine learning applications on the planet -- industry-scale recommendation models are trained with petabytes of data and serve billions of users every day. To utilize the rich user signals in the long user history, these models have been scaled up to unprecedented complexity, up to trillions of floating-point operations (TFLOPs) per example. This scale, coupled with the huge amount of training data, necessitates new storage and training algorithms to efficiently improve the quality of these complex recommendation systems.

Publication details

Journal / source
Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher
ACM
Pages
4616–4621
Publication date
15 July 2026