DeGRe is a dense‑supervised generative reranking framework designed to improve multi‑stage recommender systems by addressing label bias and credit assignment issues. It uses an offline Lookahead Evaluator with beam search to generate dense supervision signals, which are distilled into a lightweight Online Generator that can perform efficient greedy decoding at inference time. Experiments show that DeGRe outperforms baselines on public benchmarks and industrial datasets, and it has been successfully deployed on Taobao Flash Shopping to enhance online recommendations.
By Chaotian Song, Jingyao Zhang, Chenghao Chen, Zisen Sang, Dehai Zhao, Guodong Cao, Boxi Wu, Deng Cai, Jia Jia
arXiv:2607. 22665v1 Announce Type: new Abstract: Machine unlearning is becoming increasingly critical in the context of data privacy regulations, particularly for recommendation systems that are directly trained on user interaction data.
By Tushar Prakash, Brijraj Singh, Niranjan Pedanekar, Narayan Chaturvedi
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
arXiv:2609.35783v1 Announce Type: cross
Abstract: Large-scale recommender systems, particularly short-form video platforms, are often bottlenecked by massive popularity feedback loops. In such enviro...
By Arnab Bhadury, Siyan Zheng, Anlan Yu, Palaksh Rungta, Jiawei Li, Changping Meng, Dapeng Hong, Chuan He, Onkar Dalal
arXiv:2608.22979v1 Announce Type: new
Abstract: Deploying high-dimensional multimodal features in industrial recommender systems incurs substantial storage and latency overhead. Hard quantization is...
By Xiang Wang, Shigang Quan, Tingzhen Chang, Kang Yang, Sitong Chen, Yabo Fan, Xingxing Wang, Zhaodian He
arXiv:2609.23877v1 Announce Type: new
Abstract: Modern music streaming platforms face a persistent tradeoff: exploiting familiar content versus driving the exploration of novel items. While users fre...
By Xiao Liu, Yanwei Song, Srivaths Ranganathan, Yuan Chen, Zheyun Feng, Parker Steenburgh, Jochen Klingenhoefer, Nathan Lasche, Gergo Varady, Tim Steele
arXiv:2403. 00802v2 Announce Type: replace-cross Abstract: Production-grade recommender systems rely heavily on a large-scale corpus used by online media services, including Netflix, Pinterest, and Amazon.
By Amit Kumar Jaiswal
arXiv:2603.02561v2 Announce Type: replace-cross
Abstract: Attention mechanism remains the defining operator in Transformers since it provides expressive global credit assignment, yet its quadratic co...
By Chenghao Zhang, Chao Feng, Yuanhao Pu, Xunyong Yang, Wenhui Yu, Xiang Li, Chunjie Chen, Kaiqiao Zhan
arXiv:2609.40241v1 Announce Type: cross
Abstract: Large language models (LLMs) have shown promise for recommendation reranking, but their use introduces an important tradeoff between recommendation q...
By Hanjia Lyu, Yinglong Xia
SequenceO1 is an end‑to‑end framework that enables ultra‑long (up to 100K interactions) sequence modeling for recommendation systems. It compresses raw user histories into a fixed‑size sketch using Sketch Attention and then models short‑term and long‑term interests with Target‑to‑History Cross Attention. The system incorporates low‑rank caching, batching, pipeline lift, and a FlashSA kernel to keep training and inference efficient, achieving consistent offline and online performance gains when deployed at full traffic on Douyin.
By Lin Guan, Jia-Qi Yang, Zhishan Zhao, Jiaqi Huang, Hangyu Wang, Longbin Li, Beichuan Zhang, Haonan Jiang, Jinan Ni, Xiangyu Fan, Xiaowen Li, Ziyao Ren, Yuhang Qi, Xiaolong Zhu, Xuanyuan Luo, Qiwei Chen, Yi Cheng, Lele Yu
arXiv:2608. 11061v1 Announce Type: new Abstract: Large-scale neural recommender systems are typically trained with a softmax cross-entropy objective over the full item vocabulary.
By Artyom Sabitov, Daniil Volkov, Alexey Zaytsev
arXiv:2607. 24025v1 Announce Type: cross Abstract: Transformer architectures have achieved remarkable success across diverse domains; however, directly applying their standard self-attention mechanism to recommendation often yields suboptimal performance, sometimes even trailing behind well-designed simple recommendation models.
By Yu Cui, Yi Xu, Jiahao Wang, Hao Zhang, Yu Zhang, Xiaoyi Zeng, Can Wang, Jinxin Hu, Jiawei Chen