This node is currently not available in KNIME v5.3 — instead we’re showing this page for KNIME v3.7. You can use the version menu in the title bar to permanently switch your preferred version. This will also show the link to the update site.

Spark Collaborative Filtering Learner

This node utilizes the Apache Spark collaborative filtering implementation.

Notice: The matrix factorization model contains references to Spark DataFrame/RDDs and thus is not selfcontained. The referenced Spark DataFrame/RDDs will be deleted, like any other Spark DataFrame/RDD generated in KNIME, when the node is reset, or when the workflow is closed and the "Delete Spark DataFrame/RDDs on dispose" option is enabled for the current Spark context.

Options

User column: The user column. Supports long and integer columns, however the node will fail, if the actual values are outside of the integer range, due to limitations of Spark MLlib.
Product column: The product column. Supports long and integer columns, however the node will fail, if the actual values are outside of the integer range, due to limitations of Spark MLlib.
Rating column: The rating column that contains the rating of the user for the given product. Supports numeric columns.
Lambda: Specifies the regularization parameter in ALS.
Alpha: Is a parameter applicable to the implicit feedback variant of ALS that governs the baseline confidence in preference observations.
Rank: The number of latent factors in the model.
Iterations: The number of iterations to run.
Number of blocks: The number of blocks used to parallelize computation (set to -1 to auto-configure).
Implicit feedback: Select this option to use the ALS variant adapted for implicit feedback data otherwise the explicit feedback ALS variant is used.
Initialization seed: Random seed to initialize the factors.

Input Ports

: Spark DataFrame/RDD with user ratings of products

Output Ports

: The input data labeled with the prediction.
: The matrix factorization model. The model is not selfcontained and thus can not be saved to file.

Popular Predecessors

Popular Successors

Views

This node has no views

Workflows

No workflows found

Developers

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.

Installation

To use this node in KNIME, install the extension KNIME Extension for Apache Spark from the below update site following our NodePit Product and Node Installation Guide:

v3.7

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 2.4.0.v201811301556

On NodePit since: 2018-08-10

Last update: 2024-10-31

KNIME versions: From v3.6 to v3.7

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!