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Recommender
implemented for the Book Crossing demo.Recommender
implemented for the Book Crossing demo.BufferedReader.readLine()
Iterator
which iterates over any of the KDD Cup's rating files.DataModel
s into ParallelArraysSGDFactorizer
TextInputFormat
that uses a DatasetSplit.RndLineRecordReader
as a RecordReaderRecordReader
that skips some lines from the
input.ParallelArraysSGDFactorizer
IGNORED
LABEL, val1, val2, ...
CATEGORICAL, val1, val2, ...
NUMERICAL, min, max
Recommender
implemented for the GroupLens demo.DataModel
argument, which allows this Recommender
to be used with the RecommenderEvaluator
framework.
GroupLensRecommender
.Recommender
implemented for the Jester Online Joke Recommender data set demo.DataModel
which reads into memory any of the KDD Cup's rating files; it is really
meant for use with training data in the files trainIdx{1,2}}.txt.Pattern
and outputs key as given by the groupingFieldsPath
where the input documents live
The output Path
where to write the classifier as a
SequenceFile
Path
where the input documents live
The output Path
where to write the classifier as a
SequenceFile
DataModel
that reads the Netflix data set, as represented in its
unpacked file structure.Factorizer
based on Simon Funk's famous article
"Netflix Update: Try this at home".BayesFileFormatter
.TravellingSalesmanStrategy
implementation.SplitBayesInput.splitDirectory()
method is invoked
SplitBayesInput.setInputDirectory(Path)
by calling SplitBayesInput.splitFile(Path)
on each file found within that directory.
SplitBayesInput.splitFile(Path)
on each file found within that
directory.
Track1Recommender
and attempts to output the result in the correct contest format.Track2Recommender
and attempts to output the result in the correct contest format.int
.
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