arXiv AI September 1, 2026 By Tianxin Wei, Xuying Ning, Xuxing Chen, Ruizhong Qiu, Yupeng Hou, Yan Xie, Shuang Yang, Zhigang Hua, Jingrui He

CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation

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arXiv Machine Learning

Aug 11

Preserving Item Semantics for Free: Rethinking Token Initialization in LLM-Based Generative Recommendation

arXiv:2608. 07816v1 Announce Type: cross Abstract: Recent advances in generative recommendation (GR) leverage large language models (LLMs) as recommender backbones, enabling LLMs to directly generate recommendations conditioned on item-interaction histories.

By Donald Loveland, Liam Collins, Bhuvesh Kumar, Danai Koutra, Neil Shah

llms rag fine-tuning

arXiv Machine Learning

Jun 25

TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems

arXiv:2606. 25147v1 Announce Type: cross Abstract: User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors.

By Qingyun Liu, Bo Yan, Yang Liu, Yuji Roh, Ekansh Sharma, Likang Yin, Emma Olowo, Min-hsuan Tsai, Yuxuan Li, Diego Uribe, Saksham Aggarwal, Siqi Wu, Yuan Hao, Vikas Kedigehalli, Lukasz Heldt, Lichan Hong, Li Wei, Xinyang Yi

llms rag nlp

arXiv AI

Jul 29

VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation

arXiv:2607. 25209v1 Announce Type: cross Abstract: Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization.

By Shutong Qiao, Wei Yuan, Tong Chen, Hao Wang, Quoc Viet Hung Nguyen, Hongzhi Yin

llms efficiency

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.

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llms rag benchmarks

Hugging Face Trending Papers

Jun 9

Generative Archetype-Grounded Item Representations for Sequential Recommendation

Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck.

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

One email a morning, machine-written

Related stories

Preserving Item Semantics for Free: Rethinking Token Initialization in LLM-Based Generative Recommendation

TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems

VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation

Generative Archetype-Grounded Item Representations for Sequential Recommendation

Generative Archetype-Grounded Item Representations for Sequential Recommendation

HCGRec: Hint-Conditioned Generative Recommendation with Semantic IDs