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
Cross-domain recommendation (CDR) transfers preference knowledge across related domains, but federated deployment makes cross-domain alignment difficult because the behavioral anchors that align item spaces, such as overlapping users and shared interaction signals, are often sparse, unavailable, or privacy-sensitive ac...
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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