标题： 端到端个性化：将推荐系统与大型语言模型统一 显示英文标题

标题： End-to-End Personalization: Unifying Recommender Systems with Large Language Models

摘要： 推荐系统对于引导用户通过数字内容的广泛和多样化景观至关重要，它们通过提供个性化和相关的建议来帮助用户。然而，在涉及有限用户反馈或异构项目属性的情况下，提高个性化和可解释性仍然是一个挑战。在本文中，我们提出了一种新的混合推荐框架，该框架结合了图注意力网络（GATs）和大型语言模型（LLMs）以解决这些限制。首先使用LLMs通过基于标题、类型和概要等元数据生成语义上有意义的资料来丰富用户和项目表示。这些增强的嵌入作为用户和电影双部图中的初始节点特征，该图通过基于GAT的协同过滤模型进行处理。为了提高排名准确性，我们引入了一个混合损失函数，该函数结合了贝叶斯个性化排序（BPR）、余弦相似度和鲁棒的负采样。后处理涉及使用LLM对GAT生成的推荐进行重新排序，同时LLM还会生成自然语言的理由以提高透明度。我们在基准数据集上评估了我们的模型，包括MovieLens 100k和1M，其中它始终优于强基线。消融研究证实，基于LLM的嵌入和余弦相似度项对性能提升有显著贡献。这项工作展示了整合LLMs在提高推荐系统的准确性和可解释性方面的潜力。 显示英文摘要

摘要： Recommender systems are essential for guiding users through the vast and diverse landscape of digital content by delivering personalized and relevant suggestions. However, improving both personalization and interpretability remains a challenge, particularly in scenarios involving limited user feedback or heterogeneous item attributes. In this article, we propose a novel hybrid recommendation framework that combines Graph Attention Networks (GATs) with Large Language Models (LLMs) to address these limitations. LLMs are first used to enrich user and item representations by generating semantically meaningful profiles based on metadata such as titles, genres, and overviews. These enriched embeddings serve as initial node features in a user and movie bipartite graph, which is processed using a GAT based collaborative filtering model. To enhance ranking accuracy, we introduce a hybrid loss function that combines Bayesian Personalized Ranking (BPR), cosine similarity, and robust negative sampling. Post-processing involves reranking the GAT-generated recommendations using the LLM, which also generates natural-language justifications to improve transparency. We evaluated our model on benchmark datasets, including MovieLens 100k and 1M, where it consistently outperforms strong baselines. Ablation studies confirm that LLM-based embeddings and the cosine similarity term significantly contribute to performance gains. This work demonstrates the potential of integrating LLMs to improve both the accuracy and interpretability of recommender systems.