arXiv AI September 15, 2026 By Yi Chen, Rufeng Cheng, Qiang Xie, Tao Li

Generate to Explore, Select to Exploit: Aligning LLM-based Headline Generation with Personalized Recommendation

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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

Aug 13

HCGRec: Hint-Conditioned Generative Recommendation with Semantic IDs

arXiv:2608. 11980v1 Announce Type: 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

fine-tuning benchmarks safety

arXiv AI

Aug 18

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 AI

Sep 25

DeGRe: Dense-supervised Generative Reranking for Recommendation

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

rag benchmarks safety

arXiv AI

Jul 3

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

arXiv:2604. 02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation.

By Yuhang Wu, Xiangqing Shen, Fanfan Wang, Cangqi Zhou, Zhen Wu, Xinyu Dai, Rui Xia

llms rag reinforcement-learning benchmarks

arXiv AI

Sep 1

CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation

arXiv:2511.22707v2 Announce Type: replace-cross Abstract: In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific item...

By Tianxin Wei, Xuying Ning, Xuxing Chen, Ruizhong Qiu, Yupeng Hou, Yan Xie, Shuang Yang, Zhigang Hua, Jingrui He

llms rag diffusion nlp efficiency benchmarks

One email a morning, machine-written

Related stories

Generative Archetype-Grounded Item Representations for Sequential Recommendation

HCGRec: Hint-Conditioned Generative Recommendation with Semantic IDs

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

DeGRe: Dense-supervised Generative Reranking for Recommendation

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation