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  "Title": "Factorization of Sparse Counts Matrices Through Poisson\nLikelihood",
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  "Authors@R": "c(\nperson(given=\"David\", family=\"Cortes\", role=c(\"aut\", \"cre\", \"cph\"),\nemail=\"david.cortes.rivera@gmail.com\"),\nperson(given=\"Jean-Sebastien\", family=\"Roy\", role=\"cph\",\ncomment=\"Copyright holder of included tnc library\"),\nperson(given=\"Stephen\", family=\"Nash\", role=\"cph\",\ncomment=\"Copyright holder of included tnc library\")\n)",
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  "Description": "Creates a non-negative low-rank approximate factorization\nof a sparse counts matrix by maximizing Poisson likelihood with\nL1/L2 regularization (e.g. for implicit-feedback recommender\nsystems or bag-of-words-based topic modeling) (Cortes, (2018)\n<arXiv:1811.01908>), which usually leads to very sparse user\nand item factors (over 90% zero-valued). Similar to\nhierarchical Poisson factorization (HPF), but follows an\noptimization-based approach with regularization instead of a\nhierarchical prior, and is fit through gradient-based methods\ninstead of variational inference.",
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    "Date": "2026-06-19 11:06:27 UTC",
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  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2023-03-26 20:30:02 UTC",
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    "get.factor.matrices",
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    "poismf",
    "poismf_unsafe",
    "predict.poismf",
    "print.poismf",
    "summary.poismf",
    "topN",
    "topN.new"
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    {
      "page": "factors",
      "title": "Determine latent factors for new rows/users",
      "topics": [
        "factors"
      ]
    },
    {
      "page": "factors.single",
      "title": "Get latent factors for a new user given her item counts",
      "topics": [
        "factors.single"
      ]
    },
    {
      "page": "get.factor.matrices",
      "title": "Extract Latent Factor Matrices",
      "topics": [
        "get.factor.matrices"
      ]
    },
    {
      "page": "get.model.mappings",
      "title": "Extract user/row and item/column mappings from Poisson model.",
      "topics": [
        "get.model.mappings"
      ]
    },
    {
      "page": "poismf",
      "title": "Factorization of Sparse Counts Matrices through Poisson Likelihood",
      "topics": [
        "poismf"
      ]
    },
    {
      "page": "poismf_unsafe",
      "title": "Poisson factorization with no input casting",
      "topics": [
        "poismf_unsafe"
      ]
    },
    {
      "page": "predict.poismf",
      "title": "Predict expected count for new row(user) and column(item) combinations",
      "topics": [
        "predict.poismf"
      ]
    },
    {
      "page": "print.poismf",
      "title": "Get information about poismf object",
      "topics": [
        "print.poismf"
      ]
    },
    {
      "page": "summary.poismf",
      "title": "Get information about poismf object",
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        "summary.poismf"
      ]
    },
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      "page": "topN",
      "title": "Rank top-N highest-predicted items for an existing user",
      "topics": [
        "topN"
      ]
    },
    {
      "page": "topN.new",
      "title": "Rank top-N highest-predicted items for a new user",
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      ]
    }
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