优化协同过滤在大模型中的效率与扩展性，解决高成本与数据稀疏问题。
Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Models
- 融合矩阵分解与近似最近邻搜索提升计算效率
- 通过分布式架构和模型压缩实现动态更新与可扩展
- 适合需要高效推荐的大规模语言模型系统开发者
随着大语言模型（LLMs）的快速发展和个性化内容需求的增长，推荐系统在提升用户体验和促进用户参与方面变得至关重要。协同过滤算法作为众多推荐系统的核心，因其高效性和可解释性而受到广泛关注。然而，传统协同过滤方法在集成到大规模基于LLM的系统时面临诸多挑战，包括高计算成本、严重数据稀疏性、冷启动问题以及可扩展性不足。本文研究了协同过滤算法在大语言模型中的优化与可扩展性，通过先进优化策略应对这些限制。首先，分析了协同过滤算法的基本原理及其在基于LLM环境下的局限性。随后，提出矩阵分解、近似最近邻搜索和并行计算等优化技术以提升计算效率与模型精度。此外，探索了分布式架构与模型压缩策略，以在数据密集型环境中实现动态更新与可扩展性。
原文摘要 · Abstract (English)
With the rapid development of large language models (LLMs) and the growing demand for personalized content, recommendation systems have become critical in enhancing user experience and driving engagement. Collaborative filtering algorithms, being core to many recommendation systems, have garnered significant attention for their efficiency and interpretability. However, traditional collaborative filtering approaches face numerous challenges when integrated into large-scale LLM-based systems, including high computational costs, severe data sparsity, cold start problems, and lack of scalability. This paper investigates the optimization and scalability of collaborative filtering algorithms in large language models, addressing these limitations through advanced optimization strategies. Firstly, we analyze the fundamental principles of collaborative filtering algorithms and their limitations when applied in LLM-based contexts. Next, several optimization techniques such as matrix factorization, approximate nearest neighbor search, and parallel computing are proposed to enhance computational efficiency and model accuracy. Additionally, strategies such as distributed architecture and model compression are explored to facilitate dynamic updates and scalability in data-intensive environments.
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