Alternating Least Squares (ALS) for Collaborative Filtering

spark.als learns latent factors in collaborative filtering via alternating least squares. Users can call summary to obtain fitted latent factors, predict to make predictions on new data, and write.ml/read.ml to save/load fitted models.

Usage

spark.als(data, ...)

# S4 method for class 'SparkDataFrame'
spark.als(
  data,
  ratingCol = "rating",
  userCol = "user",
  itemCol = "item",
  rank = 10,
  regParam = 0.1,
  maxIter = 10,
  nonnegative = FALSE,
  implicitPrefs = FALSE,
  alpha = 1,
  numUserBlocks = 10,
  numItemBlocks = 10,
  checkpointInterval = 10,
  seed = 0
)

# S4 method for class 'ALSModel'
summary(object)

# S4 method for class 'ALSModel'
predict(object, newData)

# S4 method for class 'ALSModel,character'
write.ml(object, path, overwrite = FALSE)

Arguments

data: a SparkDataFrame for training.
...: additional argument(s) passed to the method.
ratingCol: column name for ratings.
userCol: column name for user ids. Ids must be (or can be coerced into) integers.
itemCol: column name for item ids. Ids must be (or can be coerced into) integers.
rank: rank of the matrix factorization (> 0).
regParam: regularization parameter (>= 0).
maxIter: maximum number of iterations (>= 0).
nonnegative: logical value indicating whether to apply nonnegativity constraints.
implicitPrefs: logical value indicating whether to use implicit preference.
alpha: alpha parameter in the implicit preference formulation (>= 0).
numUserBlocks: number of user blocks used to parallelize computation (> 0).
numItemBlocks: number of item blocks used to parallelize computation (> 0).
checkpointInterval: number of checkpoint intervals (>= 1) or disable checkpoint (-1). Note: this setting will be ignored if the checkpoint directory is not set.
seed: integer seed for random number generation.
object: a fitted ALS model.
newData: a SparkDataFrame for testing.
path: the directory where the model is saved.
overwrite: logical value indicating whether to overwrite if the output path already exists. Default is FALSE which means throw exception if the output path exists.

Value

spark.als returns a fitted ALS model.

summary returns summary information of the fitted model, which is a list. The list includes user (the names of the user column), item (the item column), rating (the rating column), userFactors (the estimated user factors), itemFactors (the estimated item factors), and rank (rank of the matrix factorization model).

predict returns a SparkDataFrame containing predicted values.

Details

For more details, see MLlib: Collaborative Filtering.

Note

spark.als since 2.1.0

the input rating dataframe to the ALS implementation should be deterministic. Nondeterministic data can cause failure during fitting ALS model. For example, an order-sensitive operation like sampling after a repartition makes dataframe output nondeterministic, like sample(repartition(df, 2L), FALSE, 0.5, 1618L). Checkpointing sampled dataframe or adding a sort before sampling can help make the dataframe deterministic.

summary(ALSModel) since 2.1.0

predict(ALSModel) since 2.1.0

write.ml(ALSModel, character) since 2.1.0

Examples

if (FALSE) { # \dontrun{
ratings <- list(list(0, 0, 4.0), list(0, 1, 2.0), list(1, 1, 3.0), list(1, 2, 4.0),
                list(2, 1, 1.0), list(2, 2, 5.0))
df <- createDataFrame(ratings, c("user", "item", "rating"))
model <- spark.als(df, "rating", "user", "item")

# extract latent factors
stats <- summary(model)
userFactors <- stats$userFactors
itemFactors <- stats$itemFactors

# make predictions
predicted <- predict(model, df)
showDF(predicted)

# save and load the model
path <- "path/to/model"
write.ml(model, path)
savedModel <- read.ml(path)
summary(savedModel)

# set other arguments
modelS <- spark.als(df, "rating", "user", "item", rank = 20,
                    regParam = 0.1, nonnegative = TRUE)
statsS <- summary(modelS)
} # }