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  "Title": "Factorization of Sparse Counts Matrices Through Poisson\nLikelihood",
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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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      "title": "Determine latent factors for new rows/users",
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        "factors"
      ]
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      "title": "Get latent factors for a new user given her item counts",
      "topics": [
        "factors.single"
      ]
    },
    {
      "page": "get.factor.matrices",
      "title": "Extract Latent Factor Matrices",
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    },
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      "title": "Extract user/row and item/column mappings from Poisson model.",
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      "page": "poismf",
      "title": "Factorization of Sparse Counts Matrices through Poisson Likelihood",
      "topics": [
        "poismf"
      ]
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      ]
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        "topN"
      ]
    },
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