Github user junyangq commented on a diff in the pull request:
https://github.com/apache/spark/pull/14384#discussion_r74537542
--- Diff: R/pkg/R/mllib.R ---
@@ -632,3 +642,159 @@ setMethod("predict", signature(object =
"AFTSurvivalRegressionModel"),
function(object, newData) {
return(dataFrame(callJMethod(object@jobj, "transform",
newData@sdf)))
})
+
+
+#' Alternating Least Squares (ALS) for Collaborative Filtering
+#'
+#' \code{spark.als} learns latent factors in collaborative filtering via
alternating least
+#' squares. Users can call \code{summary} to obtain fitted latent factors,
\code{predict}
+#' to make predictions on new data, and \code{write.ml}/\code{read.ml} to
save/load fitted models.
+#'
+#' For more details, see
+#'
\href{http://spark.apache.org/docs/latest/ml-collaborative-filtering.html}{MLlib:
+#' Collaborative Filtering}.
+#' Additional arguments can be passed to the methods.
+#' \describe{
+#' \item{nonnegative}{logical value indicating whether to apply
nonnegativity constraints.
+#' Default: FALSE}
+#' \item{implicitPrefs}{logical value indicating whether to use
implicit preference.
+#' Default: FALSE}
+#' \item{alpha}{alpha parameter in the implicit preference formulation
(>= 0). Default: 1.0}
+#' \item{seed}{integer seed for random number generation. Default: 0}
+#' \item{numUserBlocks}{number of user blocks used to parallelize
computation (> 0).
+#' Default: 10}
+#' \item{numItemBlocks}{number of item blocks used to parallelize
computation (> 0).
+#' Default: 10}
+#' \item{checkpointInterval}{number of checkpoint intervals (>= 1) or
disable checkpoint (-1).
+#' Default: 10}
--- End diff --
I was thinking those parameters might be not as important as the ones in
the list, and don't want the user to feel there are so many variables to tune
for the algorithm?
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