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

• URL: http://arxiv.org/abs/2601.09286v1
• Date: Wed, 14 Jan 2026 08:47:07 GMT
• Title: Why not Collaborative Filtering in Dual View? Bridging Sparse and Dense Models
• Authors: Hanze Guo, Jianxun Lian, Xiao Zhou,
• Abstract summary: Collaborative filtering remains the cornerstone of modern recommender systems.<n>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.<n>We show that aligning these dual views yields a strictly superior global SNR.
• Score: 17.01882282913444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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