Similarity-based collaborative filtering (CF) models have long demonstrated strong offline performance and conceptual simplicity. However, their scalability is limited by the quadratic cost of maintaining dense item-item similarity matrices. Partitioning-based paradigms have recently emerged as an effective strategy for balancing effectiveness and efficiency, enabling models to learn local similarities within coherent subgraphs while maintaining a limited global context. In this work, we focus on the Fine-tuning Partition-aware Similarity Refinement (FPSR) framework, a prominent representative of this family, as well as its extension, FPSR+. Reproducible evaluation of partition-aware collaborative filtering remains challenging, as prior FPSR/FPSR+ reports often rely on splits of unclear provenance and omit some similarity-based baselines, thereby complicating fair comparison. We present a transparent, fully reproducible benchmark of FPSR and FPSR+. Based on our results, the family of FPSR models does not consistently perform at the highest level. Overall, it remains competitive, validates its design choices, and shows significant advantages in long-tail scenarios. This highlights the accuracy-coverage trade-offs resulting from partitioning, global components, and hub design. Our investigation clarifies when partition-aware similarity modeling is most beneficial and offers actionable guidance for scalable recommender system design under reproducible protocols.

翻译：基于相似性的协同过滤模型长期以来展现出优异的离线性能和概念简洁性。然而，其可扩展性受限于维护稠密项-项相似度矩阵的二次方计算成本。基于分区的范式最近已成为平衡效果与效率的有效策略，使模型能够在连贯的子图中学习局部相似性，同时保持有限的全局上下文。本工作中，我们聚焦于微调分区感知相似性优化框架——该类别的重要代表FPSR及其扩展版本FPSR+。分区感知协同过滤的可复现评估仍具挑战性，因为先前的FPSR/FPSR+研究常依赖来源不明的数据划分方案，并遗漏部分基于相似性的基线模型，从而使得公平比较复杂化。我们提出了一个透明、完全可复现的FPSR与FPSR+基准测试。基于实验结果，FPSR模型系列并未持续表现出最高性能水平。总体而言，该系列仍具竞争力，验证了其设计选择，并在长尾场景中展现出显著优势。这凸显了由分区策略、全局组件和枢纽设计带来的准确率-覆盖率权衡。我们的研究明确了分区感知相似性建模最具效益的应用场景，并为可复现协议下的可扩展推荐系统设计提供了可操作的指导。