Recent progress in scaling large models has motivated recommender systems to increase model depth and capacity to better leverage massive behavioral data. However, recommendation inputs are high-dimensional and extremely sparse, and simply scaling dense backbones (e.g., deep MLPs) often yields diminishing returns or even performance degradation. Our analysis of industrial CTR models reveals a phenomenon of implicit connection sparsity: most learned connection weights tend towards zero, while only a small fraction remain prominent. This indicates a structural mismatch between dense connectivity and sparse recommendation data; by compelling the model to process vast low-utility connections instead of valid signals, the dense architecture itself becomes the primary bottleneck to effective pattern modeling. We propose SSR (Explicit Sparsity for Scalable Recommendation), a framework that incorporates sparsity explicitly into the architecture. SSR employs a multi-view "filter-then-fuse" mechanism, decomposing inputs into parallel views for dimension-level sparse filtering followed by dense fusion. Specifically, we realize the sparsity via two strategies: a Static Random Filter that achieves efficient structural sparsity via fixed dimension subsets, and Iterative Competitive Sparse (ICS), a differentiable dynamic mechanism that employs bio-inspired competition to adaptively retain high-response dimensions. Experiments on three public datasets and a billion-scale industrial dataset from AliExpress (a global e-commerce platform) show that SSR outperforms state-of-the-art baselines under similar budgets. Crucially, SSR exhibits superior scalability, delivering continuous performance gains where dense models saturate.

翻译：大规模模型的最新进展推动推荐系统增加模型深度和容量，以更好地利用海量行为数据。然而，推荐输入具有高维且极度稀疏的特性，单纯扩展密集骨干网络（如深层MLP）往往导致收益递减甚至性能退化。我们对工业级CTR模型的分析揭示了隐式连接稀疏性现象：大多数学习到的连接权重趋近于零，仅少数权重保持显著。这表明密集连接与稀疏推荐数据之间存在结构性错配——迫使模型处理大量低效用连接而非有效信号，使得密集架构本身成为有效模式建模的主要瓶颈。我们提出SSR（面向可扩展推荐系统的显式稀疏性）框架，将稀疏性显式融入架构设计。SSR采用多视角"滤波-融合"机制，将输入分解为并行视图，先进行维度级稀疏滤波再执行密集融合。具体而言，我们通过两种策略实现稀疏性：静态随机滤波器通过固定维度子集实现高效结构稀疏性，以及迭代竞争稀疏机制（ICS）——一种受生物竞争启发的可微分动态机制，自适应保留高响应维度。在三个公开数据集和来自全球电商平台AliExpress的十亿级工业数据集上的实验表明，SSR在相似预算下优于现有基线模型。关键在于，SSR展现出卓越的可扩展性，在密集模型饱和的场景中仍能持续获得性能增益。