{
  "abstract": "This study examines whether integrating structured priors with target-aware user histories improves movie rating prediction beyond supervised calibration. CDIAC combines a CatBoost prior, a target-aware Deep Interest Network (DIN) fusion branch, and ridge-based calibration (AC). A chronological MovieLens 32M protocol separates model selection, calibration, and testing and uses temporal out-of-fold priors; calibration coefficients remain fixed during inference. Across five runs on 148,738 warm-start test ratings, the model with a 20-epoch training cap achieves RMSE 0.762500 ± 0.000655 and MAE 0.570105 ± 0.000635. Compared with XGBoost + AC using identical calibration labels, the paired RMSE difference is −0.001500, with a user-cluster bootstrap 95% interval of [−0.002834, −0.000195]. Training and inference require approximately 35.09 and 1.49 times as long, respectively. Component ablations support predictive contributions within the tested pipeline, without establishing DIN-specific superiority. AC reduces aggregate RMSE but increases absolute mean signed error in all four rating groups and pooled RMSE for ratings ≤ 2. These findings indicate a modest accuracy gain at substantially higher training cost. The evidence is limited to the evaluated temporal split and warm-start population: 97.87% of test ratings involve users with at least 50 prior interactions. Feature importance and SHAP characterize only the CatBoost branch.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Film Press and Contents, Graduate School, Cheongju University, 298 Daeseong-ro, Cheongwon-gu, Cheongju 28503, Chungcheongbuk-do, Republic of Korea",
    "School of Computer Science, Xianyang Normal University, Xianyang 712000, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xiao Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Yuyu Liu",
      "orcid": "0009-0009-8562-2987"
    },
    {
      "affs": [
        1
      ],
      "name": "Hong Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ddq2rxc3acfm3.bib",
  "cited_by": 0,
  "doi": "10.3390/math14213979",
  "fulltext_url": null,
  "id": "ddq2rxc3acfm3",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/3979",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/ddq2rxc3acfm3.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:23:28Z",
      "native_id": "10.3390/math14213979",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/3979"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "A Movie Rating Prediction Method Based on Structured Prior Integration and Target-Aware User Interest with Ridge-Based Calibration",
  "title_text": "A Movie Rating Prediction Method Based on Structured Prior Integration and Target-Aware User Interest with Ridge-Based Calibration",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T00:20:49Z",
  "url": "https://research.draconitic.com/paper/ddq2rxc3acfm3",
  "venue": "Mathematics",
  "versions": []
}