Collaborative Filtering (CF) remains the cornerstone of modern recommender systems, with dense embedding--based methods dominating current practice. However, these approaches suffer from a critical limitation: our theoretical analysis reveals a fundamental signal-to-noise ratio (SNR) ceiling when modeling unpopular items, where parameter-based dense models experience diminishing SNR under severe data sparsity. To overcome this bottleneck, we propose SaD (Sparse and Dense), a unified framework that integrates the semantic expressiveness of dense embeddings with the structural reliability of sparse interaction patterns. We theoretically show that aligning these dual views yields a strictly superior global SNR. Concretely, SaD introduces a lightweight bidirectional alignment mechanism: the dense view enriches the sparse view by injecting semantic correlations, while the sparse view regularizes the dense model through explicit structural signals. Extensive experiments demonstrate that, under this dual-view alignment, even a simple matrix factorization--style dense model can achieve state-of-the-art performance. Moreover, SaD is plug-and-play and can be seamlessly applied to a wide range of existing recommender models, highlighting the enduring power of collaborative filtering when leveraged from dual perspectives. Further evaluations on real-world benchmarks show that SaD consistently outperforms strong baselines, ranking first on the BarsMatch leaderboard. The code is publicly available at https://github.com/harris26-G/SaD.

翻译：协同过滤（CF）仍是现代推荐系统的基石，其中基于稠密嵌入的方法占据着当前主流。然而，这些方法存在一个关键局限：我们的理论分析表明，在建模冷门物品时存在根本性的信噪比（SNR）上限——基于参数的稠密模型在极端数据稀疏条件下会面临信噪比衰减问题。为突破这一瓶颈，我们提出SaD（稀疏与稠密）框架，该统一框架将稠密嵌入的语义表达能力与稀疏交互模式的结构可靠性相结合。我们从理论上证明，对齐这两种视角能够获得严格更优的全局信噪比。具体而言，SaD引入了轻量级的双向对齐机制：稠密视角通过注入语义关联来增强稀疏视角，而稀疏视角则通过显式结构信号对稠密模型进行正则化。大量实验表明，在这种双视角对齐机制下，即使采用简单的矩阵分解式稠密模型也能实现最先进的性能。此外，SaD具备即插即用特性，可无缝应用于多种现有推荐模型，这彰显了从双重视角运用协同过滤的持久效力。在真实场景基准测试中的进一步评估表明，SaD持续超越各强基线模型，在BarsMatch排行榜中位列第一。代码已公开于https://github.com/harris26-G/SaD。