Name | Description | |
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fast_sampling | Fast, but memory-intensive sampling |
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fast_sampling_memory_limit | Fast sampling memory limit, in MiB |
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item_bias | Use the first item latent factor as a bias term if set to true |
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iteration_length | One iteration is iteration_length * number of entries in the training matrix |
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learn_rate | Learning rate alpha |
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random | Random number generator |
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reg_i | Regularization parameter for positive item factors |
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reg_j | Regularization parameter for negative item factors |
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reg_u | Regularization parameter for user factors |
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user_neg_items | support data structure for fast sampling |
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user_pos_items | support data structure for fast sampling |
Name | Description | |
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AddFeedback | There is no summary. |
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AddItem | There is no summary. |
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AddUser | There is no summary. |
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ComputeFit | Compute approximate fit (AUC on training data) |
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Iterate | Perform one iteration of stochastic gradient ascent over the training data |
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LoadModel | There is no summary. |
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RemoveFeedback | There is no summary. |
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RemoveItem | There is no summary. |
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RemoveUser | There is no summary. |
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ToString | There is no summary. |
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Train | There is no summary. |
Name | Description | |
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CheckSampling | There is no summary. |
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RetrainItem | Retrain the latent factors of a given item |
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RetrainUser | Retrain the latent factors of a given user |
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SampleItemPair | Sample a pair of items, given a user |
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SampleOtherItem | Sample another item, given the first one and the user |
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SampleTriple | Sample a triple for BPR learning |
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SampleUser | Sample a user that has viewed at least one and not all items |
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UpdateFactors | Update latent factors according to the stochastic gradient descent update rule |
Name | Description | |
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FastSamplingMemoryLimit | Fast sampling memory limit, in MiB |
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ItemBias | Use the first item latent factor as a bias term if set to true |
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IterationLength | One iteration is iteration_length * number of entries in the training matrix |
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LearnRate | Learning rate alpha |
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RegI | Regularization parameter for positive item factors |
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RegJ | Regularization parameter for negative item factors |
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RegU | Regularization parameter for user factors |