The paper introduces a retrieval‑grounded credit‑assignment method for generative recommenders that use Semantic IDs (SIDs). By structuring each autoregressive trace into a history summary, a set of interest hypotheses, and a final SID, a frozen retriever verifies each hypothesis as a catalog query. Rewards are assigned at the hypothesis level when any query retrieves the target within the top‑K, allowing distinct updates for rollouts that share the same SID reward and improving SID recommendation performance on Amazon Reviews datasets.
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:2604. 20861v3 Announce Type: replace-cross Abstract: Semantic IDs (SIDs) provide the discrete item vocabulary used by generative recommendation, but their quality depends on what item evidence is preserved before quantization.
By Yangchen Zeng, Jinze Wang
The paper introduces Difficulty‑Aware Semantic‑ID Optimization (DASO), a post‑training method for generative recommendation that improves tree‑structured item ranking. DASO profiles rollout groups by prefix‑match depth, reallocates a portion of candidates to prefix‑guided completions, and uses a SID‑prefix reward with an auxiliary SFT anchor to address target‑missing failures. On public benchmarks, DASO outperforms MiniOneRec‑style GRPO on 11 of 12 metrics and achieves the best results on 9 of 12 metrics, also improving level‑wise recall on an internal recommendation task.
By Xin Yu, Stephen Li, Sina Aghaei, Zifan Zhu, Jiamu Bai, Guanjie Huang, Bo Peng, Yiyao Liu, Lingzhou Xue
arXiv:2605. 17648v2 Announce Type: replace Abstract: Generative recommendation treats next-item prediction as autoregressive item-identifier generation.
By Zaiyi Zheng, Liang Wu, Guanghui Min, Yaochen Zhu, Liangjie Hong, Chen Chen, Jundong Li
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: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:2609.22227v1 Announce Type: cross
Abstract: Generative retrieval represents each item by a short Semantic ID and casts recommendation as autoregressive generation of that sequence. Because the...
By Bin Wang, Zhengyu Zhang
The paper examines LLM-based recommendation rerankers that are often evaluated under an oracle protocol, which guarantees the ground-truth item is present in the scored set. Across Amazon datasets, this protocol overestimates realistic NDCG@10 by 92–95% because realistic retrieval only covers 2–19% of relevant items at K=100, creating a recall ceiling that limits any closed-candidate reranker's top‑k NDCG. The authors find that various optimisation strategies—including prompt engineering, model scaling, sequential models, supervised neural rerankers, LoRA fine‑tuning, hybrid retrieval, score‑aware prompting, and LLM+CF fusion—do not significantly improve over a collaborative‑filtering baseline under realistic retrieval, and they propose a Recall‑Aware Evaluation Protocol (RAEP) to better assess rerankers in low‑recall regimes.
By Zhaohui Wang
arXiv:2609.00638v1 Announce Type: cross
Abstract: Retrieval is the first stage of modern search and advertising systems, selecting a candidate set from a large item universe for downstream ranking an...
By Runpeng Dai, Kaili Huang, Changsung Kang, Ciya Liao
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
arXiv:2606. 29947v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as rerankers in recommender systems, with the expectation that semantic understanding will help in cold-start and long-tail regimes.
By Zhe Dong (University of Maine at Presque Isle), Fang Qin (Stanford University), Manish Shah (Independent Researcher), Yicheng Wang (Independent Researcher)