Define a storage configuration
component

Select the configuration component to be used to provide the configuration
information for the connection to the target file system such as HDFS.

If you leave this check box clear, the target file system is the local
system.

The configuration component to be used must be present in the same Job. For
example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write
the result in a given HDFS system.

Feature table

This map allows tASLModel to read the
right type of data for each required factor.

Training percentage

Enter the percentage (expressed in the decimal form) of the input data
to be used to train the recommender model. The rest of the data is used
to test the model.

Number of latent factors

Enter the number of the latent factors, with which each user or
product feature is measured.

Number of iterations

Enter the number of iterations you want the Job to perform to train
the model.

This number should be smaller than 30 in order to avoid stack overflow
issues and in practices, the convergent score (RMSE score) can often be
obtained before you have to use a number beyond 30.

However, if you need to perform more than 30 iterations, you must
increase the stack size used to run the Job; to do this, you can add the
-Xss argument, for example -Xss2048k,
to the JVM Settings table in the
Advanced settings tab of the
Run view. For further information
about the JVM Settings table, see

Talend Studio User
Guide.

Regularization factor

Enter the regularization number you want to use to avoid
overfitting.

Build model for implicit feedback data
set

Select this check box to enable tALSModel to handle the implicit data sets.

Contrary to the explicit data sets such as the ranking of a product,
an implicit data set only implies users’ preferences, for example, a
record showing how frequently a user is buying a certain item.

If you leave this check box clear, tALSModel handles the explicit data sets only.

For related details about how the ALS model handles the implicit data
sets, see the documentation of Spark in the following link:https://spark.apache.org/docs/latest/mllib-collaborative-filtering.html.

Confidence coefficient for implicit
training

Enter the number to indicate the level of confidence you have in the
observed user preferences.

Parquet model path

Enter the directory in which you need to store the generated
recommender model in the file system to be used.

The button for browsing does not work with the Spark Local mode; if you are using the Spark Yarn or the Spark Standalone mode,
ensure that you have properly configured the connection in a configuration component in
the same Job, such as tHDFSConfiguration.

Parquet model name

Enter the name you need to use for the recommender model.

Usage rule

This component is used as an end component and requires an input link.

Note that the parameters you need to set are free parameters and so
their values may be provided by previous experiments, empirical guesses
or the like. They do not have any optimal values applicable for all
datasets. Therefore, you need to train the model you are generating with
different sets of parameter values until you can obtain the minimum RMSE
score. This score is outputted in the console of the Run view each time a Job execution is done.

MLlib installation

In Apache Spark V1.3 or earlier versions of Spark, the Spark machine
learning library, MLlib, uses the gfortran
runtime library. You need to ensure that this library is
already present in every node of the Spark cluster to be used.

For further information about MLlib and this library, see the related
documentation from Spark.

RMSE score

These scores are output along with the other Log4j INFO-level information. If you want to
prevent outputting the irrelevant information, you can, for example, change the Log4j level
of this kind of information to WARN but note you need to keep this DataScience Logger code as INFO.

If you are using a subscription-based version of the Studio, the activity of this
component can be logged using the log4j feature. For more
information on this feature, see
Talend Studio User Guide.

For more information on the log4j logging levels, see the Apache documentation at http://logging.apache.org/log4j/1.2/apidocs/org/apache/log4j/Level.html.