GARDRec：面向大语言模型推荐的决策级图接地方法
GARDRec: Decision-Level Graph Grounding for Large Language Model Recommendation
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中文总结 AI 辅助
本文针对现有图增强型LLM推荐器排序决策受用户-物品关系约束弱的问题，提出GARDRec框架，通过图接地技术提升下一项推荐的候选排序性能，经实验验证其优于代表性基线。
中文摘要 AI 辅助
大语言模型（LLM）可通过自然语言提示解释物品描述、用户指令及外部知识，为推荐任务带来新机遇。但现有图增强型LLM推荐器通常仅将知识图谱作为提示级证据，导致排序决策受结构化用户-物品关系的约束较弱，这对下一项推荐任务存在问题——该任务要求模型在保留时间偏好、协同信号及属性匹配的同时，在同一用户语境下对比候选物品。为解决此问题，本文提出GARDRec，这是一种面向基于LLM的下一项排序任务的图接地自适应推理与决策感知推荐框架。GARDRec从文本节点特征与图传播中构建语义-结构物品表示，从时间加权历史与一阶邻域中推导个性化图上下文，并通过连续多模态提示将图衍生表示与冻结的LLM对齐；其通过后期决策分支注入显式交互与匹配特征，同时利用候选间注意力与受限生成似然支持最终排序。在三个公共基准及多个LLM主干上开展的实验表明，GARDRec相比代表性基线总体提升了候选排序性能； ablation与诊断分析验证了图投影、邻域检索、显式决策特征、排序损失及生成校准的贡献。
英文摘要
Large language models (LLMs) offer new opportunities for recommendation by interpreting item descriptions, user instructions, and external knowledge through natural-language prompts. However, existing graph-augmented LLM recommenders often use knowledge graphs mainly as prompt-level evidence, leaving ranking decisions weakly constrained by structured user-item relations. This is problematic for next-item recommendation, where the model must compare candidates under the same user context while preserving temporal preference, collaborative signals, and attribute matches. To address this issue, we propose \emph{GARDRec}, a Graph-grounded Adaptive Reasoning and Decision-aware Recommendation framework for LLM-based next-item ranking. GARDRec constructs semantic-structural item representations from textual node features and graph propagation, derives personalized graph contexts from temporally weighted histories and first-order neighborhoods, and aligns graph-derived representations with a frozen LLM through continuous multimodal prompts. Explicit interaction and matching features are injected through late-stage decision branches, while inter-candidate attention and restricted generative likelihood support final ranking. Experiments on three public benchmarks with multiple LLM backbones show that GARDRec generally improves candidate-ranking performance over representative baselines. Ablation and diagnostic analyses verify the contributions of graph projection, neighborhood retrieval, explicit decision features, ranking loss, and generative calibration.
