tRecommend - 6.2

Talend Components Reference Guide

EnrichVersion
6.2
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Data Integration
Talend Data Management Platform
Talend Data Services Platform
Talend ESB
Talend MDM Platform
Talend Open Studio for Big Data
Talend Open Studio for Data Integration
Talend Open Studio for Data Quality
Talend Open Studio for ESB
Talend Open Studio for MDM
Talend Real-Time Big Data Platform
task
Data Governance
Data Quality and Preparation
Design and Development
EnrichPlatform
Talend Studio

Function

tRecommend uses a given recommender model to analyse user data incoming from its preceding Spark component so as to estimate the preferences of these users.

Purpose

Based on the user-product recommender model generated by tASLModel, tRecommend recommends products to users known to this model.

Depending on the Talend solution you are using, this component can be used in one, some or all of the following Job frameworks:

tRecommend properties in Spark Batch Jobs

Component family

Machine Learning / Recommendation

 
 

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.

Note that 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.

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 in Spark Batch Jobs

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

You need to use the Spark Configuration tab in the Run view to define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, one and only one file system related component from the Storage family is required in the same Job so that Spark can use this component to connect to the file system to which the jar files dependent on the Job are transferred:

This connection is effective on a per-Job basis.

Log4j

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.

Related scenarios

No scenario is available for the Spark Batch version of this component yet.

tRecommend properties in Spark Streaming Jobs

Warning

The streaming version of this component is available in the Palette of the studio on the condition that you have subscribed to Talend Real-time Big Data Platform or Talend Data Fabric.

Component family

Machine Learning / Recommendation

 
 

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.

Note that 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.

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 in Spark Streaming Jobs

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

You need to use the Spark Configuration tab in the Run view to define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, one and only one file system related component from the Storage family is required in the same Job so that Spark can use this component to connect to the file system to which the jar files dependent on the Job are transferred:

This connection is effective on a per-Job basis.

Log4j

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.

Related scenarios

No scenario is available for the Spark Streaming version of this component yet.