REGER is a unified framework that enhances generative recommendation by explicitly modeling inter-item relations through semantic pretraining, history encoding, and SID generation. It pretrains the model to generate semantic neighbors’ IDs, uses combined global and local attention to capture historical context, and introduces a hierarchical preference decomposition over the SID prefix tree. The approach also distills a discriminative teacher’s full conditional distributions to improve next-token predictions, yielding consistent performance gains on real-world datasets.
By Yuxi Huang, Silin Zhou, Chenhao Wang, Zihan Li, Gangming Guo, Pei Ke, Shen Gao, Shuo Shang
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
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
The paper introduces Evo-Rec, a three‑stage framework that improves generative recommendation by learning better reasoning traces for Semantic ID (SID) generation. It first aligns SIDs with textual and behavioral contexts, then selects candidate reasoning traces that improve ground‑truth item prediction, and finally refines the reasoning policy via reinforcement learning with catalog‑constrained generation and ranking‑aware feedback. Experiments on Amazon Review datasets show Evo‑Rec consistently outperforms existing discriminative, generative, and reasoning‑enhanced recommenders across all metrics.
By Mengdan Zhu, Yufan Zhao, Sophie Di, Yao Zhao, Tao Di, Yulan Yan, Sridhar Iyer, Liang Zhao
The paper introduces Evo-Rec, a three‑stage framework that improves generative recommendation by learning better reasoning traces for Semantic ID (SID) generation. First, it aligns SIDs with textual and behavioral contexts; second, it samples and selects reasoning traces that improve ground‑truth item prediction via supervised fine‑tuning; third, it refines the reasoning policy with reinforcement learning using catalog‑constrained generation and ranking‑aware feedback. Experiments on three Amazon Review datasets show Evo‑Rec consistently outperforms discriminative, generative, and other reasoning‑enhanced recommenders across all metrics.
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:2603. 23183v2 Announce Type: replace-cross Abstract: Recent advances in generative recommendation have leveraged pretrained LLMs by formulating sequential recommendation as autoregressive generation over a unified token space comprising language tokens and itemic identifiers, where each item is represented by a compact sequence of discrete tokens, namely Semantic IDs (SIDs).
By Yingzhi He, Yan Sun, Junfei Tan, Yuxin Chen, Xiaoyu Kong, Chunxu Shen, Xiang Wang, An Zhang, Tat-Seng Chua
The paper introduces API, a framework that adapts pretrained interpretable features for generative recommendation. By reusing sparse autoencoder‑derived feature indices as a reusable semantic ID vocabulary, API avoids additional vocabulary learning and preserves item distinctions even with limited token budgets. The Semantic Prediction Adapter learns user preferences from interaction histories, combining feature‑index and strength predictions to score candidates, while also providing explainable retrieval through feature contributions linked to pretrained semantic annotations.
By Ziluowen Luo, Ruochen Liu, Jun Yin, Hao Yan, Zhengxuan Hu, Xinze Shi, Chaozhuo Li, Chengqi Zhang, Senzhang Wang
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
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
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
arXiv:2510. 21805v2 Announce Type: replace-cross Abstract: Generative recommendation (GR) is an emerging paradigm that represents each item via a tokenizer as an n-digit semantic ID (SID) and predicts the next item by autoregressively generating its SID conditioned on the user's history.
By Zhao Liu, Yichen Zhu, Yiqing Yang, Xiao Lv, Guoping Tang, Rui Huang, Qiang Luo, Ruiming Tang, Kun Gai, Guorui Zhou