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
Generative Recommendation (GenRec) represents a promising paradigm that achieves remarkable empirical success by encoding items as compact Semantic IDs (SIDs) and modeling user behavior via next-token prediction across diverse recommendation scenarios. Extending this paradigm to cross-domain recommendation is challengi...
Haiying He, Xiao-Peng Li, Yuchen Gu et al.· 0 citations
A series of models are explored that connect item Semantic IDs with natural language in a unified representation space and seek to unlock the potential of natural-language chain-of-thought (CoT) reasoning for recommendation, including OneReason.
Jiang-Xia Cao, Hao Peng, Wen-Long Xu et al.· 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
This work proposes WebSwarm, a progressive recursive delegation framework that jointly constructs task decomposition, recursive expansion, and agent collaboration during inference and consistently outperforms single-agent and multi-agent baselines on deep, wide, and interleaved deep-and-wide tasks.
