Schema and Edit
Schema

A schema is a row description. It defines the number of fields (columns) to
be processed and passed on to the next component. The schema is either Built-In or stored remotely in the Repository.

Click Edit schema to make changes to the schema.
If the current schema is of the Repository type, three
options are available:

• View schema: choose this option to view the
  schema only.

• Change to built-in property: choose this
  option to change the schema to Built-in for
  local changes.

• Update repository connection: choose this
  option to change the schema stored in the repository and decide whether to propagate
  the changes to all the Jobs upon completion. If you just want to propagate the
  changes to the current Job, you can select No
  upon completion and choose this schema metadata again in the [Repository Content] window.

Note that apart from the columns you can edit by yourself, product_ID and score columns are read-only
and used to carry the data about the user preferences calculated against the recommender
model being used. The score column indicates how strongly
recommended a product is to a given user.

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.

Input parquet model

Enter the directory where the recommender model to be used is
stored. This directory must be in the machine where the Job is
run.

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.

This model should be generated by a tALSModel component.

Select the User Identity
column

Select the column that is carrying the user ID data from the input
columns.

This tRecommend component needs
the input user IDs to match the users known to the recommender model to be used.

Number of recommendations

Enter the number of the most recommended products to be
outputted.

Note that this is a numeric value and so you cannot use the double
quotation marks around it.

Usage rule

This component is used as an intermediate step.

This component, along with the Spark Batch component Palette it belongs to, appears only
when you are creating a Spark Batch Job.

Note that in this documentation, unless otherwise
explicitly stated, a scenario presents only Standard Jobs,
that is to say traditional
Talend
data integration Jobs.

The user IDs processed by this component must be known to the recommender model to be
used. When a user is unknown to the recommender model, the corresponding values returned in
the product_ID and the score columns are null. This allows you to retrieve the records about the
unknown users using a tFilterRow component after tRecommend in the same Job.

MLlib installation

Spark’s machine learning library, MLlib, uses the gfortran runtime library and for this
reason, 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.

Spark Connection

This connection is effective on a per-Job basis.

Schema and Edit
Schema

A schema is a row description. It defines the number of fields (columns) to
be processed and passed on to the next component. The schema is either Built-In or stored remotely in the Repository.

Click Edit schema to make changes to the schema.
If the current schema is of the Repository type, three
options are available:

• View schema: choose this option to view the
  schema only.

• Change to built-in property: choose this
  option to change the schema to Built-in for
  local changes.

• Update repository connection: choose this
  option to change the schema stored in the repository and decide whether to propagate
  the changes to all the Jobs upon completion. If you just want to propagate the
  changes to the current Job, you can select No
  upon completion and choose this schema metadata again in the [Repository Content] window.

Note that apart from the columns you can edit by yourself, product_ID and score columns are read-only
and used to carry the data about the user preferences calculated against the recommender
model being used. The score column indicates how strongly
recommended a product is to a given user.

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.

Input parquet model

Enter the directory where the recommender model to be used is
stored. This directory must be in the machine where the Job is
run.

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.

This model should be generated by a tALSModel component.

Select the User Identity
column

Select the column that is carrying the user ID data from the input
columns.

This tRecommend component needs
the input user IDs to match the users known to the recommender model to be used.

Number of recommendations

Enter the number of the most recommended products to be
outputted.

Note that this is a numeric value and so you cannot use the double
quotation marks around it.

Usage rule

This component is used as an intermediate step.

This component, along with the Spark Streaming component Palette it belongs to, appears
only when you are creating a Spark Streaming Job.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents
only Standard Jobs, that is to say traditional
Talend
data
integration Jobs.

The user IDs processed by this component must be known to the recommender model to be
used. When a user is unknown to the recommender model, the corresponding values returned in
the product_ID and the score columns are null. This allows you to retrieve the records about the
unknown users using a tFilterRow component after tRecommend in the same Job.

MLlib installation

Spark’s machine learning library, MLlib, uses the gfortran runtime library and for this
reason, 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.

Spark Connection

This connection is effective on a per-Job basis.