Large Language Models (LLMs) have shown great potential for enhancing recommender systems through their extensive world knowledge and reasoning capabilities. However, effectively translating these semantic signals into traditional collaborative embeddings remains an open challenge. Existing approaches typically fall into two extremes: direct inference methods are computationally prohibitive for large-scale retrieval, while embedding-based methods primarily focus on unilateral feature augmentation rather than holistic collaborative signal enhancement. To bridge this gap, we propose Topology-Augmented Graph Collaborative Filtering (TAGCF), a novel framework that transforms semantic knowledge into topological connectivity. Unlike existing approaches that depend on textual features or direct interaction synthesis, TAGCF employs LLMs to infer interaction intents and underlying causal relationships from user-item pairs, representing these insights as intermediate attribute nodes within an enriched User-Attribute-Item (U-A-I) graph. Furthermore, to effectively model the heterogeneous relations in this augmented structure, we propose Adaptive Relation-weighted Graph Convolution (ARGC), which employs relation-specific prediction networks to dynamically estimate the importance of each relation type. Extensive experiments across multiple benchmark datasets and CF backbones demonstrate consistent improvements, with comprehensive evaluations including cold-start scenarios validating the effectiveness and robustness of our framework. All code will be made publicly available. For anonymous review, our code is available at the following anonymous link: https://anonymous.4open.science/r/AGCF-2441353190/.

翻译：大语言模型凭借其广泛的世界知识和推理能力，在增强推荐系统方面展现出巨大潜力。然而，如何将这些语义信号有效转化为传统的协同嵌入仍然是一个开放性问题。现有方法通常陷入两个极端：直接推理方法在大规模检索场景下计算成本过高，而基于嵌入的方法主要侧重于单边特征增强而非整体协同信号提升。为弥合这一差距，我们提出拓扑增强图协同过滤框架，这是一种将语义知识转化为拓扑连接性的新型框架。与依赖文本特征或直接交互合成的现有方法不同，TAGCF利用大语言模型从用户-物品对中推断交互意图和潜在因果关系，并将这些洞察表示为增强型用户-属性-物品图中的中间属性节点。此外，为有效建模这一增强结构中的异质关系，我们提出自适应关系加权图卷积方法，该方法采用关系特定的预测网络动态估计每种关系类型的重要性。在多个基准数据集和协同过滤骨干模型上的大量实验表明，该方法取得了持续的性能提升，包括冷启动场景在内的综合评估验证了我们框架的有效性和鲁棒性。所有代码将公开提供。为匿名评审，代码可通过以下匿名链接获取：https://anonymous.4open.science/r/AGCF-2441353190/。