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.
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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