Generative Recommendation has emerged as a promising paradigm, reformulating recommendation as a sequence-to-sequence generation task over hierarchical Semantic IDs. However, current approaches face a severe challenge that we define as Semantic Drift, where errors in early, high-level tokens irreversibly divert the gen...
Cheng-Cheng Guo, Kuo Cai, Yu Zhou et al.· Proceedings of the 20th ACM...· 0 citations
Summary-token architectures such as Kwai Summary Attention (KSA) compress distant context into learnable Summary token per chunk, and hybrid variants interleave these layers with full attention to retain exact access to the original text. In such hybrid KSA-Full architecture, the interleaving inserted summary tokens ar...
OneRec Team, Chenglong Chu, Guo-Hong Mu et al.· 0 citations
The proposed Uncertainty-Calibrated MOPD (UCMOPD), which addresses limitations of standard MOPD through two complementary mechanisms, improves the general-capability average over standard MOPD by $4.48\% and $7.86\% while maintaining vertical-domain performance.
Zi-Yuan Liu, Jiao Ou, Jian Liang et al.· 0 citations
This work comprehensively surveys recent generative recommendation advances through a tri-decoupled perspective, summarize the evolution of tokenization strategies, analyze the trade-offs of major generative architectures, and summarize the transition from supervised next-token prediction to reinforcement-learning-base...
Xiao-Peng Li, Yejing Wang, Hong-Hui Bao et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes Promise, a novel framework that integrates dense, step-by-step verification into generative models, and unlocks Test-Time Scaling Laws in recommender systems, demonstrating that by increasing inference compute, smaller models can match or surpass larger models.
Cheng-Cheng Guo, Kuo Cai, Yu Zhou et al.· Proceedings of the 20th ACM...· 0 citations
Results show that generative retrieval can combine shared modeling with objective-specific control and complementary candidate generation, and under the same 512-item retrieval budget, Multi-Decoder OneRec improves over the single-decoder OneRec baseline.
Counteraction-Aware Multi-Teacher On-Policy Distillation is proposed, which addresses issues with decoupled alternating training and gap-based sample selection and outperforms all baselines in general capability recovery while maintaining domain-specific behavior.
Tian-Le Chen, Jiao Ou, Zi-Yuan Liu et al.· arXiv.org· 13 citations· ⚡1
In the current digital ecosystem, recommender systems serve as the core infrastructure for navigating large-scale content catalogs and delivering personalized services, typically following multi-stage discriminative pipelines (e.g., retrieval, ranking, and re-ranking). However, their fragmented architectures cause casc...
Xiaopeng Li, Yejing Wang, Honghui Bao et al.· Proceedings of the 32nd ACM...· 0 citations
WhisperRec compresses teacher-generated CoT into learnable latent reasoning tokens, enabling a Latent-Reason-then-Answer paradigm that performs reasoning in latent space without producing verbose rationales, and achieves over 10x higher online inference throughput.
Hao Jiang, Pei Du, Pengfei Yao et al.· arXiv.org· 0 citations
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