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Fix matrix factorization trainer's doc based on user feedback #3170
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Original file line number | Diff line number | Diff line change |
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@@ -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: | ||
/// </para> | ||
/// <code language="csharp"> | ||
/// // 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; | ||
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@@ -65,7 +60,7 @@ namespace Microsoft.ML.Trainers | |
/// <i>R</i> is approximated by the product of <i>P</i>'s transpose and <i>Q</i>. This trainer implements | ||
/// <a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/mf_adaptive_pakdd.pdf'>a stochastic gradient method</a> for finding <i>P</i> | ||
/// and <i>Q</i> via minimizing the distance between<i> R</i> and the product of <i>P</i>'s transpose and Q.</para>. | ||
/// <para>For users interested in the mathematical details, please see the references below.</para> | ||
/// <para>The underlying library used in ML.NET matrix factorization can be found on <a href='https://github.com/cjlin1/libmf'>a Github repository</a>. For users interested in the mathematical details, please see the references below.</para> | ||
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on the libmf Github repository |
||
/// <list type = 'bullet'> | ||
/// <item> | ||
/// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_journal.pdf' > A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems</a></description> | ||
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@@ -76,6 +71,9 @@ namespace Microsoft.ML.Trainers | |
/// <item> | ||
/// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_open_source.pdf' > LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems</a></description> | ||
/// </item> | ||
/// <item> | ||
/// <description><a href='https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf' > Selection of Negative Samples for One-class Matrix Factorization</a></description> | ||
/// </item> | ||
/// </list> | ||
/// </remarks> | ||
/// <example> | ||
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tangential, what do you think about #3072