Pack: Experimentation for recommender extension templates

Description:

This is a train/test set example used in experimentation.

Data in this set consists of three columns, one column for user identification, one for item identification, and one for rating. Every dataset is accompanied with the appropriate aml file.

This dataset can be used for rating prediction and item recommendation operator testing. Item recommendation operators will ignore the rating column.

Description:

This workflow reads train/test dataset from a specified RapidMiner repository and tests selected operator on that datasets. Only datasets specified with a proper regular expression are considered. Train and test data filenames must correspond e.g (train1, test1). Informations about training and testing data, performanse measures of a selected operator are stored as an Excel file.

Note: Train/test file names should not be contained in the repository path. E.g training/train is not a god path, while data/train is.

Description:

This is a help file in how to use experimentation workflows with a recommendation operators.

Description:

This is a dataset iteration workflow. It is a part of Experimentation workflow for recommender extension.

Loop FIles operator iterates through datasets from a specified directory using read aml operator. Only datasets specified with a proper regular expression are considered. Train and test data filenames must correspond e.g (train1.aml, test1.aml).

In each iteration Loop Files calles specified operator testing workflow with Execute subprocess operator.

Informations about training and testing data, performanse measures of a selected operator are stored as an Excel file.

For this workflow to work user should specify the path to the train/test datasets in the GenerateMacro operator.

Description:

This is a metafeature extraction workflow used in Experimentation workflow for recommender extension operators.

This workflow extracts metadata from the train/test datasets (user/item counts, rating count, sparsity etc).

This workflow is called from the operator testing workflow using Execute Process operator.

Description:

This workflow is used for operator testing. It joins dataset metafeatures with execution times and performanse measures of the selected recommendation operator.

In the Extract train and Extract test Execute Process operator user should open Metafeature extraction workflow.

In the Loop Operator train/test data are used to evaluate performanse of the selected operator. Result is remebered and joined with the time and metafeature informations.

This workflow can be used both for Item Recommendation and Rating Prediction operator testing. All needed changes are to be made in the Loop Operator.