Platform-scale recommender systems often span multiple business streams such as organic recommendation, advertising, and merchant services, where user behaviors form a continuous cross-stream trajectory. Maintaining separate ranking systems fragments user representations and increases engineering cost. We propose \text...
Yinqi Zhang, Pei-Yu Hu, Yuntian Tang et al.· 1 citation
Agentic recommender systems use large language models to maintain semantic memory and support evidence-aware recommendation. However, existing memory mechanisms often compress user and item information into coarse summaries and connect them with scalar collaborative links, making it difficult to preserve fine-grained p...
Pei-Yu Hu, Wei-Hai Lu, Si-Ying Gu 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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