Recommender systems often struggle with data sparsity and cold-start scenarios, limiting their ability to provide accurate suggestions for new or infrequent users. This paper presents a Graph Attention Network (GAT) based Collaborative Filtering (CF) framework enhanced with Large Language Model (LLM) driven context aware embeddings. Specifically, we generate concise textual user profiles and unify item metadata (titles, genres, overviews) into rich textual embeddings, injecting these as initial node features in a bipartite user item graph. To further optimize ranking performance, we introduce a hybrid loss function that combines Bayesian Personalized Ranking (BPR) with a cosine similarity term and robust negative sampling, ensuring explicit negative feedback is distinguished from unobserved data. Experiments on the MovieLens 100k and 1M datasets show consistent improvements over state-of-the-art baselines in Precision, NDCG, and MAP while demonstrating robustness for users with limited interaction history. Ablation studies confirm the critical role of LLM-augmented embeddings and the cosine similarity term in capturing nuanced semantic relationships. Our approach effectively mitigates sparsity and cold-start limitations by integrating LLM-derived contextual understanding into graph-based architectures. Future directions include balancing recommendation accuracy with coverage and diversity, and introducing fairness-aware constraints and interpretability features to enhance system performance further.

翻译：推荐系统常面临数据稀疏性和冷启动场景的挑战，限制了其为新用户或低频用户提供准确推荐的能力。本文提出一种基于图注意力网络（GAT）的协同过滤（CF）框架，该框架通过大型语言模型（LLM）驱动的上下文感知嵌入进行增强。具体而言，我们生成简洁的文本用户画像，并将物品元数据（标题、类型、概述）统一为丰富的文本嵌入，将其作为二分用户-物品图中的初始节点特征注入。为进一步优化排序性能，我们引入一种混合损失函数，该函数将贝叶斯个性化排序（BPR）与余弦相似度项及鲁棒的负采样相结合，确保显式负反馈与未观测数据得以区分。在MovieLens 100k和1M数据集上的实验表明，该方法在精确率、归一化折损累计增益（NDCG）和平均准确率（MAP）上均优于当前最先进的基线模型，同时对交互历史有限的用户展现出鲁棒性。消融研究证实了LLM增强嵌入与余弦相似度项在捕捉细微语义关系中的关键作用。我们的方法通过将LLM衍生的上下文理解整合至基于图的架构中，有效缓解了稀疏性与冷启动限制。未来研究方向包括平衡推荐准确性与覆盖率及多样性，并引入公平性约束与可解释性特征以进一步提升系统性能。