#' @title slimrec
#' @description Sparse Linear Method to Predict Ratings and Top-N
#' Reccomendations
#' @details \strong{Sparse linear method}
#' (\href{http://glaros.dtc.umn.edu/gkhome/node/774}{DOI:
#' 10.1109/ICDM.2011.134}) predicts ratings and top-n recommendations suited
#' for sparse implicit positive feedback systems. SLIM is decomposed into
#' multiple elasticnet optimization problems which are solved in parallel over
#' multiple cores. The package is based on "SLIM: Sparse Linear Methods for
#' Top-N Recommender Systems" by Xia Ning and George Karypis.
#'
#' The method predicts ratings of a user for a given item as a linear
#' combination ratings of all other items provided by the user. The
#' coefficients for an item are determined elastic-net regression (both L1 and
#' L2 regularization) over ratings matrix.
#'
#' The optimization problem solves:
#'
#' \deqn{\min_{c_{j,.}} \frac{1}{2} \|a_{j,.} - Ac_{j,.}\|^2_{2} +
#' \frac{\beta}{2} \|c_{j,.}\|^2_{2} + \gamma \|c_{j,.}\|_{1}} subject to
#' \eqn{c_{j,j} = 0} and optional non-negative constraint \eqn{c_{j,.} >= 0}
#' where \eqn{a_{j,.}} is the j th column of the input ratings matrix and
#' \eqn{c_{j,.}} is the j th column of the coefficient matrix(to be
#' determined).
#'
#' The method assumes that unknown rating values to be zero. Hence, it is
#' primarily designed for implicit feeback mechanisms, but not restricted
#' them. The main use of the ratings is to generate top-n lists of users and
#' items.
#'
#' The package provides three functions: \itemize{ \item \code{slim}: Function
#' to compute ratings and coefficient matrix for the sparse ratings matrix
#' using SLIM method. \item \code{tune_slim}: Function to arrive at an optimal
#' value of \code{alpha} for \code{\link{slim}}. \item \code{top_rows/cols}:
#' Functions to find row/column numbers corresponding the largest values in a
#' particular column/row of a matrix. This is helpful to generate top-n users
#' or items as recommendations. }
#'
#' The package is primarily based on the wonderful package \pkg{glmnet} by
#' Jerome Friedman, Trevor Hastie, Noah Simon, Rob Tibshirani.
#'
#' If you intend to use SLIM method for large matrices( around >= 1e7
#' ratings), this package might be slow enough to be practically useful even
#' in parallel mode. You might want to look at \pkg{biglasso} and other
#' implementations like \href{http://librec.net/}{librec}.
#'
#' @import assertthat
#' @import parallel
#' @import glmnet
#' @import Matrix
#' @import bigmemory
#' @import pbapply
#' @importFrom stats runif
#'
"_PACKAGE"
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