arXiv:2607. 24995v1 Announce Type: new Abstract: Semantic IDs (SIDs) are now a central component of generative recommendation.
By Junting Wang, Xinrui He, Yunzhe Li, Hari Sundaram
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
EPIC: Explicit Posterior Item Conditioning for Semantic ID Diffusion Recommendation proposes a method that adds explicit item-level competition into the denoising process of Semantic ID (SID) generative recommendation. The approach builds a personalized posterior over feasible candidate items based on the current generation context and the user's recent interactions, then projects this distribution back to unresolved SID positions to guide subsequent token decisions. Experiments on four Amazon benchmarks show consistent improvements over strong baselines, with diagnostic analyses indicating that the gains stem from personalized transition evidence that preserves promising item hypotheses during denoising.
By Tuan-Binh Tran, Thanh Tam Nguyen, Quoc Viet Hung Nguyen, Dung D. Le, Tung Kieu, Thanh Trung Huynh
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
arXiv:2607. 25216v1 Announce Type: cross Abstract: Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating semantic IDs.
By Ziyu Zheng, Zhengshun Du, Yaming Yang, Bin Tong, Guan Wang, Meng Yan, Ziyu Guan, Wei Zhao
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