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 other Spark Yarn
modes that the Studio supports with your distribution, ensure that you have properly
configured the connection in a configuration component in the same Job, such as

tHDFSConfiguration
. Use the
configuration component depending on the filesystem to be used.

Parquet model name

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

Set Checkpoint Interval

Set the frequency of checkpoints. It is recommended to leave the default
value (10).

Before setting a value for this parameter, activate checkpointing and set
the checkpoint directory in the Spark
Configuration tab of the Run
view.

For further information about checkpointing the
activities of your Apache Spark Job, see the documentation on Talend Help Center (https://help.talend.com).

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.