arXiv Machine Learning

CADET: Context-Conditioned Ads CTR Prediction With a Decoder-Only Transformer

arXiv:2602. 11410v2 Announce Type: replace Abstract: Click-through rate (CTR) prediction is fundamental to online advertising systems.

arXiv AI

1d ago

UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation

arXiv:2608. 16797v1 Announce Type: cross Abstract: Industrial recommenders rely on two model families that have evolved largely independently: feature-interaction models over multi-field user/item features, and sequential models over user-behavior histories.

By Rongcheng Lin, Yan Sun, Jamey Zhang, Guanglei Xiong, Ivan Ji, Xianjie Chen, Shujian Bu

rag efficiency

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

Jun 10

DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction

arXiv:2606. 10243v1 Announce Type: new Abstract: Offsite conversion rate (OCVR) prediction is an important ranking problem in computational recommendation systems.

By Reazul Hasan Russel, Mingwei Tang, Rostam Shirani, Xinlong Liu, Navid Madani, Leo Ding, Yawen He, Xiangyu Wang, Mustafa Acar, Ashish Katiyar, Yuhai Li, Alan Yang, Metarya Ruparel, Derek Qiang Xu, Rupert Wu, Rui Yang, Liang Tao, Xinyi Zhao, Larry Zhang, Sri Reddy, Rob Malkin

llms rag

arXiv Machine Learning

Jul 17

Deep-learning Causal Retrieval Optimization for Efficient e-commerce Distribution in Pinterest

arXiv:2607. 14161v1 Announce Type: cross Abstract: Pinterest is where people turn inspiration into action as users browse ideas, then take steps toward realization, often by discovering shoppable content.

By Junpeng Hou, XianXing Zhang, Sai Xiao, Derek Cheng, Darren Reger, Olafur Gudmundsson, Mehdi Ben Ayed, Zhiqing Rao, Huizhong Duan

arXiv Machine Learning

Jul 31

Building a User Foundation Model for the Open Web

arXiv:2607. 28019v1 Announce Type: new Abstract: User foundation models have demonstrated strong results in e-commerce and social recommendation, but most industrial deployments assume environments where user identity is stable and persistent.

By Solal Vernier, Ivan Can Arisoy, Merwan Barlier, Bla\v{z} \v{S}krlj

llms fine-tuning safety

arXiv Machine Learning

Aug 4

Learning What to Remember: Test-Time Training via Context Distillation

arXiv:2608. 01672v1 Announce Type: cross Abstract: Effective long-context modeling is not merely about retaining more of the past, but about preserving the information that may prove relevant later.

By Zixuan Wang, Xingyu Dang, Rui-Jie Zhu, Zixin Wen, Hengyu Fu, Wenhao Chai, Jason D. Lee

llms efficiency

arXiv AI

1d ago

When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction

arXiv:2608. 15592v1 Announce Type: new Abstract: Efficient LLM serving is often bottlenecked by the need to pad sequences to a fixed maximum length, and this wastes compute and degrades throughput.

By Feiyang Ren, Shengtao Wen, Lingbing Guo, Yu Tian, Yuanning Cui, Xiang Chen

llms reinforcement-learning efficiency benchmarks

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 AI

1d ago

Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation

arXiv:2608. 11980v2 Announce Type: replace-cross Abstract: Semantic-ID generative recommenders represent each item as a short sequence of discrete semantic tokens and predict the next item by autoregressively generating this token sequence.

By Kangning Zhang, Haotian Fang, Xukun Luo, Hao Yin, Yang Gao, Peng Yan, Weiwen Liu, Weinan Zhang, Yong Yu

reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 16

Next-Latent Prediction Transformers Learn Compact World Models

arXiv:2511. 05963v4 Announce Type: replace Abstract: Transformers replace recurrence with a memory that grows with sequence length and self-attention that enables ad-hoc lookups over past tokens.

By Jayden Teoh, Manan Tomar, Kwangjun Ahn, Edward S. Hu, Tim Pearce, Pratyusha Sharma, Akshay Krishnamurthy, Riashat Islam, Alex Lamb, John Langford

llms diffusion benchmarks safety

arXiv AI

Jul 15

Action-Aware Generative Sequence Modeling for Short Video Recommendation

arXiv:2604. 25834v2 Announce Type: replace Abstract: With the rapid development of the Internet, users have increasingly higher expectations for the recommendation accuracy of online content consumption platforms.

By Wenhao Li, Zihan Lin, Zhengxiao Guo, Jie Zhou, Shukai Liu, Yongqi Liu, Chuan Luo, Chaoyi Ma, Ruiming Tang, Han Li

arXiv Machine Learning

Jul 31

ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation

arXiv:2607. 27744v1 Announce Type: new Abstract: Modern recommendation models gain prediction quality by scaling feature-interaction and sequence modules, but production cost constraints cap how far systems can scale.

By Yuxin Chen, Liang Luo, Buyun Zhang, Jian Jiao, Boda Li, Haoyu Wang, Tongyi Tang, Ao Cai, Zijian Shen, Zhengkai Zhang, Wenyi Xie, Ryan Dick, Han Liu, Neng Shi, Bin Yu, Jianbo Xiao, Shuyao Bi, Hongtao Yu, Yuanwei Fang, Zhuoran Zhao, Sijia Chen, Yang Chen, Shuqi Yang, Qianru Li, Zikun Liu, Wei Ling, Sihan Zeng, Longhao Jin, Jiaxin Lu, Yinbin Ma, Jiawei Li, Yichen Ruan, Yong Ler Lee, Birmingham Guan, Zijian Li, Jianbo Sun, Zhengyu Zhang, Zeliang Chen, Xiaohan Wei, Yuchen Hao, GP Musumeci, Venkatesh Ranganathan, Yantao Yao, Chunqiang Tang, Wenlin Chen, Santanu Kolay, Ellie Dingqiao Wen

benchmarks

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