The proposed OneModel, a unified framework for multi-stream final ranking, maps heterogeneous behaviors into shared event sequences, learns long-context user representations with an action-oriented backbone, and introduces scenario-aware Information Modulation to balance cross-stream transfer and stream-specific specia...
Yinqi Zhang, Pei-Yu Hu, Yuntian Tang et al.· 1 citation
Experiments show that \textsc{AtomRec} consistently outperforms state-of-the-art agentic and memory-augmented baselines, with around 8.5\% average relative improvement across metrics.
Pei-Yu Hu, Wei-Hai Lu, Si-Ying Gu et al.· 0 citations
FedCGR is proposed, a federated generative CDR framework that keeps the item language stable and makes adaptation explicit and consistently outperforms federated generative baselines and achieves competitive performance against strong sequential and federated CDR methods under both full-ranking and sampled evaluation p...
Zhuo-Dong Liu, Hugen Lv, Xiang-Yu Li et al.· 0 citations
To further optimize the reasoning trajectory, HiLaR combines final recommendation feedback with layer-aware process rewards derived from the marginal target-likelihood gain of each state, and generally outperforms strong sequential, generative, and LLM-based recommendation baselines.
Pei-Yu Hu, Si-Ying Gu, Wei-Hai Lu et al.· arXiv.org· 1 citation
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