arXiv Machine Learning August 5, 2026 By Juhee Hong, Meng Liu, Shengzhi Wang, Jin Zhou, Xiaoheng Mao, Zhao Zhu, Ruochen Liu, Huihui Cheng, Leon Gao, Christopher Leung, Chandra Mouli Sekar, Yijia Liu, Boyang Yu, Tuan Trieu, Dawei Sun, Jeet Kanjani, Rui Li, Jing Qian, Xuan Cao, Minjie Fan, Mingze Gao

Target-Aware Early Stage Ranking

Read the original on arXiv Machine Learning →

arXiv:2511. 21095v2 Announce Type: replace Abstract: Early Stage Ranking (ESR) in large-scale recommendation systems is dominated by ''user--item decoupling'' Two Tower architectures, which scale efficiently but cannot capture fine-grained, target-aware user--item interactions directly.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

efficiency

arXiv AI

Jul 29

REPREC: Representation Driven Parameter-Efficient Recommendation System

arXiv:2607. 24845v1 Announce Type: cross Abstract: Large language models (LLMs) have been applied to sequential recommendation by formulating it as a natural language task.

By Harshini Kavuru, Dwipam Katariya, Giri Iyengar, Pranab Mohanty, Kalanand Mishra, Kalanand Mishra

llms rag fine-tuning efficiency benchmarks safety

arXiv Machine Learning

Jun 2

UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale

arXiv:2606. 00422v1 Announce Type: cross Abstract: Modern recommendation systems predominantly train retrieval and ranking as separate models despite both increasingly relying on large transformers encoding the same user behavior data, duplicating parameters, compute, and serving cost.

By Hanyu Li, Yi-Ping Hsu, Aditya Mantha, Prabhat Agarwal, Laksh Bhasin, Jialu Wang, Hongtao Lin, Bella Huang, Yaxin Li, Xinyi Li, Chuxi Wang, Kousik Rajesh, Hooshmand Shokri Razaghi, Shunyao Li, Zongyue Qin, Jaewon Yang, James Li, Dhruvil Deven Badani, Jiajing Xu, Charles Rosenberg

llms efficiency

arXiv Machine Learning

Jul 31

Recall Before You Rank: Similarity-Guided Top-$K$ Reuse for Efficient Long-Context Attention

arXiv:2607. 27692v1 Announce Type: cross Abstract: Top-$K$ sparse attention reduces the cost of Softmax and value aggregation by attending to only a small subset of key--value (KV) entries.

By Wenshuai Yao, Wenyong Zhou, Hanyong Shao, Yizhe Chen, Zhiyuan Ning, Yuannuo Feng, Ru Huang, Kechao Tang

efficiency

arXiv AI

Jun 30

CMSL: Constructive Multi-Sequence Learning for Recommendation Systems

arXiv:2606. 28533v1 Announce Type: cross Abstract: Sequence learning has emerged as the promising paradigm in recommendation systems, surpassing traditional Deep Learning Recommendation Models (DLRM) by capturing the temporal nuances of user behavior.

By Zikun Cui, Renzhi Wu, Junjie Yang, Li Sheng, Jijie Wei, Linfeng Liu, Tai Guo, Tao Jia, Xiaodong Wang, Hong Li, Li Yu, Sri Reddy, Hong Yan

llms diffusion benchmarks

arXiv Machine Learning

Jun 10

Generative Archetype-Grounded Item Representations for Sequential Recommendation

arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.

By Yifan Li, Jiahong Liu, Xinni Zhang, Hao Chen, Yankai Chen, Wenhao Yu, Jianting Chen, Irwin King

llms rag benchmarks

arXiv AI

Jul 21

WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture

arXiv:2607. 17017v1 Announce Type: cross Abstract: As scalability becomes increasingly important in recommendation modeling, recent architectures have advanced the modeling of two broad sources of ranking signals along separate paths: non-sequence features, including user, item, context, and cross features; and sequence features from user behavior histories.

By Renqin Cai, Dawei Sun, Yuanjun Yao, Zhiyong Wang, Velvin Fu, Maggie Zhuang, Yu Shi, Zhongnan Fang, Xuan Cao, Jing Qian, Rui Li

efficiency

Related stories

REPREC: Representation Driven Parameter-Efficient Recommendation System

UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale

Recall Before You Rank: Similarity-Guided Top-$K$ Reuse for Efficient Long-Context Attention

CMSL: Constructive Multi-Sequence Learning for Recommendation Systems

Generative Archetype-Grounded Item Representations for Sequential Recommendation

WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture