Name | Description | |
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global_bias | The bias (global average) |
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item_factors | Matrix containing the latent item factors |
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learn_rate | Learn rate |
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num_factors | Number of latent factors |
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regularization | Regularization parameter |
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user_factors | Matrix containing the latent user factors |
Name | Description | |
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MatrixFactorization | There is no summary. |
Name | Description | |
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AddItem | There is no summary. |
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AddRating | There is no summary. |
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AddUser | There is no summary. |
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ComputeFit | Compute approximated fit (RMSE) on the training data |
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Iterate | There is no summary. |
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LoadModel | There is no summary. |
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Predict | Predict the rating of a given user for a given item |
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RemoveItem | There is no summary. |
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RemoveRating | There is no summary. |
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RemoveUser | There is no summary. |
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RetrainItem | Updates the latent factors of an item |
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RetrainUser | Updates the latent factors on a user |
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SaveModel | There is no summary. |
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ToString | There is no summary. |
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Train | There is no summary. |
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UpdateRating | There is no summary. |
Name | Description | |
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Iterate | Iterate once over rating data and adjust corresponding factors (stochastic gradient descent) |
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Predict | There is no summary. |
Name | Description | |
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InitMean | Mean of the normal distribution used to initialize the factors |
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InitStdev | Standard deviation of the normal distribution used to initialize the factors |
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LearnRate | Learn rate |
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NumFactors | Number of latent factors |
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NumIter | Number of iterations over the training data |
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Regularization | Regularization parameter |