对比多种推荐算法在大数据下的表现，找最优平衡点。
A Comparative Study of Recommender Systems under Big Data Constraints
- 对比EASE-R、SLIM、FunkSVD等6种主流推荐模型。
- EASE-R和RP3Beta在精度与可扩展性上表现最佳。
- 适合对实时性要求高、数据量大的系统选型参考。
推荐系统已成为电商、流媒体、新闻和社交媒体等数字服务的核心工具。随着用户-物品交互数据呈指数增长，尤其在大数据环境下，选择合适的推荐模型成为关键挑战。本文对比了包括EASE-R、SLIM、SLIM-ElasticNet、矩阵分解（FunkSVD和ALS）、P3Alpha和RP3Beta在内的多种前沿推荐算法，从可扩展性、计算复杂度、预测精度和可解释性等维度进行评估。分析涵盖其理论基础与实际应用场景。结果表明，尽管SLIM及SLIM-ElasticNet在精度与可解释性方面表现优异，但其计算开销过高，不适用于实时系统；而EASE-R与RP3Beta在性能与可扩展性间取得良好平衡，更适于大规模环境。本研究旨在为特定大数据约束与系统需求提供推荐模型选型指导。
原文摘要 · Abstract (English)
Recommender Systems (RS) have become essential tools in a wide range of digital services, from e-commerce and streaming platforms to news and social media. As the volume of user-item interactions grows exponentially, especially in Big Data environments, selecting the most appropriate RS model becomes a critical task. This paper presents a comparative study of several state-of-the-art recommender algorithms, including EASE-R, SLIM, SLIM with ElasticNet regularization, Matrix Factorization (FunkSVD and ALS), P3Alpha, and RP3Beta. We evaluate these models according to key criteria such as scalability, computational complexity, predictive accuracy, and interpretability. The analysis considers both their theoretical underpinnings and practical applicability in large-scale scenarios. Our results highlight that while models like SLIM and SLIM-ElasticNet offer high accuracy and interpretability, they suffer from high computational costs, making them less suitable for real-time applications. In contrast, algorithms such as EASE-R and RP3Beta achieve a favorable balance between performance and scalability, proving more effective in large-scale environments. This study aims to provide guidelines for selecting the most appropriate recommender approach based on specific Big Data constraints and system requirements.
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