cran 0.5.2

DESCRIPTION

@@ -1,15 +1,15 @@
 Package: rsparse
 Type: Package
 Title: Statistical Learning on Sparse Matrices
-Version: 0.5.1
+Version: 0.5.2
 Authors@R: c(
-    person("Dmitriy", "Selivanov", role=c("aut", "cre", "cph"), email="[email protected]", 
+    person("Dmitriy", "Selivanov", role=c("aut", "cre", "cph"), email="[email protected]", 
     comment = c(ORCID = "0000-0001-5413-1506")),
     person("David", "Cortes", role="ctb"),
     person("Drew", "Schmidt", role="ctb", comment="configure script for BLAS, LAPACK detection"), 
     person("Wei-Chen", "Chen", role="ctb", comment="configure script and work on linking to float package")
     )
-Maintainer: Dmitriy Selivanov <[email protected]>
+Maintainer: Dmitriy Selivanov <[email protected]>
 Description: Implements many algorithms for statistical learning on 
   sparse matrices - matrix factorizations, matrix completion, 
   elastic net regressions, factorization machines. 
@@ -27,7 +27,7 @@ Description: Implements many algorithms for statistical learning on
   (2005, <doi:10.1145/1102351.1102441>)
   3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, 
   Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder 
-  et al. (2014, <arXiv:1410.2596>)
+  et al. (2014, <doi:10.48550/arXiv.1410.2596>)
   4) Linear-Flow matrix factorization, from 'Practical linear models for 
   large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al 
   (2016, ISBN:978-1-57735-770-4)
@@ -55,7 +55,7 @@ Suggests:
     testthat, 
     covr
 StagedInstall: TRUE
-URL: https://github.com/rexyai/rsparse
-BugReports: https://github.com/rexyai/rsparse/issues
-RoxygenNote: 7.2.1
+URL: https://github.com/dselivanov/rsparse
+BugReports: https://github.com/dselivanov/rsparse/issues
+RoxygenNote: 7.3.1
 NeedsCompilation: yes

R/SoftALS.R

@@ -2,7 +2,7 @@
 #' @description Fit SoftImpute/SoftSVD via fast alternating least squares. Based on the
 #' paper by Trevor Hastie, Rahul Mazumder, Jason D. Lee, Reza Zadeh
 #' by "Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares" -
-#' \url{https://arxiv.org/pdf/1410.2596.pdf}
+#' \url{http://arxiv.org/pdf/1410.2596}
 #' @param x sparse matrix. Both CSR \code{dgRMatrix} and CSC \code{dgCMatrix} are supported.
 #' CSR matrix is preffered because in this case algorithm will benefit from multithreaded
 #' CSR * dense matrix products (if OpenMP is supported on your platform).

R/model_ScaleNormalize.R

@@ -2,7 +2,7 @@
 #' @description scales input user-item interaction matrix as per eq (16) from the paper.
 #' Usage of such rescaled matrix with [PureSVD] model will be equal to running PureSVD
 #' on the scaled cosine-based inter-item similarity matrix.
-#' @references See \href{https://arxiv.org/pdf/1511.06033.pdf}{EigenRec: Generalizing PureSVD for
+#' @references See \href{http://arxiv.org/pdf/1511.06033}{EigenRec: Generalizing PureSVD for
 #' Effective and Efficient Top-N Recommendations} for details.
 #' @export
 ScaleNormalize = R6::R6Class(

README.md

@@ -1,10 +1,9 @@
 # rsparse <img src='man/figures/logo.png' align="right" height="128" />
 <!-- badges: start -->
-[![R build status](https://github.com/rexyai/rsparse/workflows/R-CMD-check/badge.svg)](https://github.com/rexyai/rsparse/actions)
-[![codecov](https://codecov.io/gh/rexyai/rsparse/branch/master/graph/badge.svg)](https://codecov.io/gh/rexyai/rsparse/branch/master)
+[![R build status](https://github.com/rexyai/rsparse/workflows/R-CMD-check/badge.svg)](https://github.com/dselivanov/rsparse/actions)
+[![codecov](https://codecov.io/gh/rexyai/rsparse/branch/master/graph/badge.svg)](https://app.codecov.io/gh/rexyai/rsparse/branch/master)
 [![License](https://eddelbuettel.github.io/badges/GPL2+.svg)](http://www.gnu.org/licenses/gpl-2.0.html)
 [![Project Status](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html#maturing)
-<a href="https://rexy.ai"><img src="https://s3-eu-west-1.amazonaws.com/rexy.ai/images/favicon.ico" height="32" width="32"></a>
 <!-- badges: end -->
 
 `rsparse` is an R package for statistical learning primarily on **sparse matrices** -  **matrix factorizations, factorization machines, out-of-core regression**. Many of the implemented algorithms are particularly useful for **recommender systems** and **NLP**. 
@@ -15,10 +14,10 @@ We've paid some attention to the implementation details - we try to avoid data c
 
 ### Classification/Regression
 
-1. [Follow the proximally-regularized leader](http://proceedings.mlr.press/v15/mcmahan11b/mcmahan11b.pdf) which allows to solve **very large linear/logistic regression** problems with elastic-net penalty. Solver uses stochastic gradient descent with adaptive learning rates (so can be used for online learning - not necessary to load all data to RAM). See [Ad Click Prediction: a View from the Trenches](https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf) for more examples.
+1. [Follow the proximally-regularized leader](http://proceedings.mlr.press/v15/mcmahan11b/mcmahan11b.pdf) which allows to solve **very large linear/logistic regression** problems with elastic-net penalty. Solver uses stochastic gradient descent with adaptive learning rates (so can be used for online learning - not necessary to load all data to RAM). See [Ad Click Prediction: a View from the Trenches](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41159.pdf) for more examples.
     - Only logistic regerssion implemented at the moment
     - Native format for matrices is CSR - `Matrix::RsparseMatrix`. However common R `Matrix::CsparseMatrix` (`dgCMatrix`) will be converted automatically.
-1. [Factorization Machines](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf) supervised learning algorithm which learns second order polynomial interactions in a factorized way. We provide highly optimized SIMD accelerated implementation.  
+1. [Factorization Machines](https://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/Rendle2010FM.pdf) supervised learning algorithm which learns second order polynomial interactions in a factorized way. We provide highly optimized SIMD accelerated implementation.  
 
 ### Matrix Factorizations
 
@@ -32,14 +31,14 @@ See details in [Applications of the Conjugate Gradient Method for Implicit Feedb
     * <img src="https://raw.githubusercontent.com/rexyai/rsparse/master/docs/img/WRMF.png" width="400">
 1. **Linear-Flow** from [Practical Linear Models for Large-Scale One-Class Collaborative Filtering](http://www.bkveton.com/docs/ijcai2016.pdf). Algorithm looks for factorized low-rank item-item similarity matrix (in some sense it is similar to [SLIM](http://glaros.dtc.umn.edu/gkhome/node/774))
     * <img src="https://raw.githubusercontent.com/rexyai/rsparse/master/docs/img/LinearFlow.png" width="300">
-1. Fast **Truncated SVD** and **Truncated Soft-SVD** via Alternating Least Squares as described in [Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares](https://arxiv.org/pdf/1410.2596.pdf). Works for both sparse and dense matrices. Works on [float](https://github.com/wrathematics/float) matrices as well! For certain problems may be even faster than [irlba](https://github.com/bwlewis/irlba) package.
+1. Fast **Truncated SVD** and **Truncated Soft-SVD** via Alternating Least Squares as described in [Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares](http://arxiv.org/pdf/1410.2596). Works for both sparse and dense matrices. Works on [float](https://github.com/wrathematics/float) matrices as well! For certain problems may be even faster than [irlba](https://github.com/bwlewis/irlba) package.
     * <img src="https://raw.githubusercontent.com/rexyai/rsparse/master/docs/img/soft-svd.png" width="600">
-1. **Soft-Impute** via fast Alternating Least Squares as described in [Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares](https://arxiv.org/pdf/1410.2596.pdf).
+1. **Soft-Impute** via fast Alternating Least Squares as described in [Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares](https://arxiv.org/pdf/1410.2596).
     * <img src="https://raw.githubusercontent.com/rexyai/rsparse/master/docs/img/soft-impute.png" width="400">
     * with a solution in SVD form <img src="https://raw.githubusercontent.com/rexyai/rsparse/master/docs/img/soft-impute-svd-form.png" width="150">
 1. **GloVe** as described in [GloVe: Global Vectors for Word Representation](https://nlp.stanford.edu/pubs/glove.pdf).
     * This is usually used to train word embeddings, but actually also very useful for recommender systems.
-1. Matrix scaling as descibed in [EigenRec: Generalizing PureSVD for Effective and Efficient Top-N Recommendations](https://arxiv.org/pdf/1511.06033.pdf)
+1. Matrix scaling as descibed in [EigenRec: Generalizing PureSVD for Effective and Efficient Top-N Recommendations](http://arxiv.org/pdf/1511.06033)
 
 *********************
 
@@ -84,14 +83,6 @@ By default, R for Windows comes with unoptimized BLAS and LAPACK libraries, and
 **Note that syntax is these posts/slides is not up to date since package was under active development**
 
 1. [Slides from DataFest Tbilisi(2017-11-16)](https://www.slideshare.net/DmitriySelivanov/matrix-factorizations-for-recommender-systems)
-1. [Introduction to matrix factorization with Weighted-ALS algorithm](http://dsnotes.com/post/2017-05-28-matrix-factorization-for-recommender-systems/) - collaborative filtering for implicit feedback datasets.
-1. [Music recommendations using LastFM-360K dataset](http://dsnotes.com/post/2017-06-28-matrix-factorization-for-recommender-systems-part-2/)
-    * evaluation metrics for ranking
-    * setting up proper cross-validation
-    * possible issues with nested parallelism and thread contention
-    * making recommendations for new users
-    * complimentary item-to-item recommendations
-1. [Benchmark](http://dsnotes.com/post/2017-07-10-bench-wrmf/) against other good implementations
 
 Here is example of `rsparse::WRMF` on [lastfm360k](https://www.upf.edu/web/mtg/lastfm360k) dataset in comparison with other good implementations: