From 95f09fde4bdec993d40640f72cacf1c7fdda0e03 Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Tue, 2 Apr 2019 09:23:13 -0700 Subject: [PATCH] Update description about key type and add one more reference --- .../MatrixFactorizationTrainer.cs | 24 +++++++++---------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs b/src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs index 571287aaa0..dee4c412eb 100644 --- a/src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs +++ b/src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs @@ -31,24 +31,19 @@ namespace Microsoft.ML.Trainers /// and the value at the location specified by the two indexes. For an example data structure of a tuple, one can use: /// /// - /// // The following variables defines the shape of a m-by-n matrix. The variable firstRowIndex indicates the integer that - /// // would be mapped to the first row index. If user data uses 0-based indices for rows, firstRowIndex can be set to 0. - /// // Similarly, for 1-based indices, firstRowIndex could be 1. - /// const int firstRowIndex = 1; - /// const int firstColumnIndex = 1; + /// // The following variables defines the shape of a m-by-n matrix. Indexes start with 0; that is, our indexing system + /// // is 0-based. /// const int m = 60; /// const int n = 100; /// /// // A tuple of row index, column index, and rating. It specifies a value in the rating matrix. /// class MatrixElement /// { - /// // Matrix column index starts from firstColumnIndex and is at most firstColumnIndex+n-1. - /// // Contieuous=true means that all values from firstColumnIndex to firstColumnIndex+n-1 are allowed keys. - /// // [KeyType(Contiguous = true, Count = n, Min = firstColumnIndex)] - /// // public uint MatrixColumnIndex; - /// // Matrix row index starts from firstRowIndex and is at most firstRowIndex+m-1. - /// // Contieuous=true means that all values from firstRowIndex to firstRowIndex+m-1 are allowed keys. - /// [KeyType(Contiguous = true, Count = m, Min = firstRowIndex)] + /// // Matrix column index starts from 0 and is at most n-1. + /// [KeyType(n)] + /// public uint MatrixColumnIndex; + /// // Matrix row index starts from 0 and is at most m-1. + /// [KeyType(m)] /// public uint MatrixRowIndex; /// // The rating at the MatrixColumnIndex-th column and the MatrixRowIndex-th row. /// public float Value; @@ -65,7 +60,7 @@ namespace Microsoft.ML.Trainers /// R is approximated by the product of P's transpose and Q. This trainer implements /// a stochastic gradient method for finding P /// and Q via minimizing the distance between R and the product of P's transpose and Q.. - /// For users interested in the mathematical details, please see the references below. + /// The underlying library used in ML.NET matrix factorization can be found on a Github repository. For users interested in the mathematical details, please see the references below. /// /// /// A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems @@ -76,6 +71,9 @@ namespace Microsoft.ML.Trainers /// /// LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems /// + /// + /// Selection of Negative Samples for One-class Matrix Factorization + /// /// /// ///