ReliGRec：基于用户风险感知提示路由的可靠性导向的LLM生成式推荐
ReliGRec: Reliability-Oriented LLM-Based Generative Recommendation via User-Risk-Aware Prompt Routing
AI总结：
针对推荐系统中用户行为异质导致的鲁棒性问题，提出ReliGRec框架，利用双视图弱风险估计器融合行为与图令牌，在推理时路由选择简单或谨慎提示，将弱风险信号转化为生成控制信号，提升推荐质量与鲁棒性。
AI中文摘要：
真实世界推荐系统中的用户行为是异质的。虽然一些用户表现出连贯的偏好，但其他用户则表现出突然的兴趣转变、突发性交互、过度重复或与协同邻域不一致。这些偏差可能源于良性变化或操纵（包括托攻击），但仅凭这些并不足以确定恶意意图。现有的鲁棒推荐器通过训练时重加权或图聚合来利用用户风险信号，而在基于LLM的生成式推荐中，将生成过程适应于估计的用户级弱风险仍未被充分探索。我们提出了ReliGRec（可靠性导向的生成式推荐），一个弱监督框架，其名称表示其设计目标而非监督可靠性变量。ReliGRec从评论反馈信号中为一部分用户推导用户级弱风险代理标签，并分别使用行为令牌和时间图令牌来表示序列行为和协同上下文。一个双视图弱风险估计器融合这些表示以产生用户级弱风险分数，该分数在推理时选择简单或谨慎提示。谨慎提示旨在鼓励关注稳定的、协同支持的证据，同时减少对孤立的、短期的或重复交互的过度依赖。行为令牌通过弱风险估计和路由影响生成，而聚合的图令牌为下一项语义ID生成提供协同上下文。因此，ReliGRec将弱风险估计从辅助预测转变为生成时的控制信号。实验报告了具有竞争力的推荐和弱风险代理标签预测性能，而路由分析则表征了弱风险引导提示的推荐质量和推理成本行为。
英文摘要：
User behavior in real-world recommender systems is heterogeneous. While some users exhibit coherent preferences, others show abrupt interest shifts, bursty interactions, excessive repetition, or inconsistency with collaborative neighborhoods. Such deviations may arise from benign variation or manipulation, including shilling attacks, but do not alone establish malicious intent. Existing robust recommenders exploit user-risk signals through training-time reweighting or graph aggregation, whereas adapting generation to estimated user-level weak risk remains underexplored in LLM-based generative recommendation. We propose ReliGRec (Reliability-oriented Generative Recommendation), a weakly supervised framework whose name denotes its design goal rather than a supervised reliability variable. ReliGRec derives user-level weak-risk proxy labels from review-feedback signals for a subset of users and represents sequential behavior and collaborative context using a Behavior Token and temporal Graph Tokens, respectively. A Dual-View Weak-Risk Estimator fuses the representations to produce a user-level weak-risk score that selects a Simple or Cautious Prompt at inference. The Cautious Prompt is designed to encourage attention to stable, collaboratively supported evidence while reducing overreliance on isolated, short-term, or repeated interactions. The Behavior Token affects generation through weak-risk estimation and routing, whereas the aggregated Graph Token provides collaborative context for next-item Semantic ID generation. ReliGRec thus turns weak-risk estimation from an auxiliary prediction into a generation-time control signal. Experiments report competitive recommendation and weak-risk proxy-label prediction, while routing analyses characterize the recommendation-quality and inference-cost behavior of weak-risk-guided prompting.
