Why Not Collaborative Filtering in Dual View? Bridging Sparse and Dense Models

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 Sparse and Dense (SaD) , 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 CF when leveraged from dual perspectives. Further evaluations on real-world benchmarks show that SaD consistently outperforms strong baselines, ranking first on the BarsMatch leaderboard ( https://openbenchmark.github.io/BARS/Matching/leaderboard/index.html ). The code is publicly available at https://github.com/harris26-G/SaD .