{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T04:22:17Z","timestamp":1778559737008,"version":"3.51.4"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"name":"Public Computing Cloud of Renmin University of China"},{"name":"Building World-Class Universities (Disciplines) at Renmin University of China"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    <jats:bold>Collaborative Filtering (CF)<\/jats:bold>\n                    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\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">Signal-to-Noise Ratio (SNR)<\/jats:italic>\n                    <\/jats:bold>\n                    <jats:italic toggle=\"yes\">ceiling<\/jats:italic>\n                    when modeling unpopular items, where parameter-based dense models experience diminishing SNR under severe data sparsity. To overcome this bottleneck, we propose\n                    <jats:bold>Sparse and Dense (SaD)<\/jats:bold>\n                    , 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\u2013style 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 (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/openbenchmark.github.io\/BARS\/Matching\/leaderboard\/index.html\">https:\/\/openbenchmark.github.io\/BARS\/Matching\/leaderboard\/index.html<\/jats:ext-link>\n                    ). The code is publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/harris26-G\/SaD\">https:\/\/github.com\/harris26-G\/SaD<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3789509","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T14:06:50Z","timestamp":1771855610000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Why Not Collaborative Filtering in Dual View? 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