{
  "annotation_id": "2bee440a-cf13-4811-a29c-2b30ab27986a",
  "date_created": "2026-02-17T05:53:20.502000Z",
  "date_modified": "2026-02-17T05:53:20.502000Z",
  "file_hash": "c51f9ca4861cdb7991471cc0dc746236fc610bd634485b424046cc93840c5214",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.09286",
    "authors": [
      "Hanze Guo",
      "Jianxun Lian",
      "Xiao Zhou"
    ],
    "categories": [
      "cs.IR",
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Why not Collaborative Filtering in Dual View? Bridging Sparse and Dense Models",
    "url": "https://arxiv.org/abs/2601.09286",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "9949c721-d5e1-4503-9804-1f596274d376",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}