User: Matej Mihelčić

Hybrid recommendation system (1)

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This is one hybrid recommendation system combining linear regression recommender, created using RapidMiner core operators, and Recommender extension multiple collaborative filtering and attribute based operators.

Created: 2012-05-17

Credits: Matej Mihelčić Matko Bošnjak

Parameter optimization (1)

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This is a parameter optimization workflow for rating prediction recommendation operators.

Created: 2012-02-10 | Last updated: 2012-02-10

Credits: Matej Mihelčić

Experimentation through repository access (1)

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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,...

Created: 2012-01-31 | Last updated: 2012-02-01

Credits: Matej Mihelčić Matko Bošnjak tomS

Data iteration workflow (RP) (1)

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This is a data iteration workflow used to iterate throug query update sets.

Created: 2012-01-29

Credits: Matej Mihelčić Matko Bošnjak

Model update workflow (RP) (1)

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This is a Model update workflow called from data iteration workflow on every given query set. In the Loop operator model and current training set are retrieved from the repository. Model update is performed on a given query set creating new model. Model and updated train set are saved in the repository.

Created: 2012-01-29 | Last updated: 2012-01-30

Credits: Matej Mihelčić

recommender workflow (RP) (1)

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This is a main online update experimentation workflow. It consists of three Execute Process operators. First operator executes model training workflow. Second operator executes online updates workflow for multiple query update sets. The last operator executes performance testing and comparison workflow. Final performance results are saved in an Excel file.

Created: 2012-01-29

Credits: Matej Mihelčić

Model testing workflow (RP) (1)

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This workflow measures performance of three models. Model learned on train data and upgraded using online model updates. Model learned on train data + all query update sets. Model learned on train data only.

Created: 2012-01-29 | Last updated: 2012-01-30

Credits: Matej Mihelčić

Model saving workflow (RP) (1)

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This workflow trains and saves model for a selected rating prediction operator.

Created: 2012-01-29 | Last updated: 2012-01-30

Credits: Matej Mihelčić

Model update workflow (1)

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This is a Model update workflow called from data iteration workflow on every given query set. In the Loop operator model and current training set are retrieved from the repository. Model update is performed on a given query set creating new model. Model and updated train set are saved in the repository.

Created: 2012-01-29 | Last updated: 2012-01-29

Credits: Matej Mihelčić Matko Bošnjak

Data iteration workflow (1)

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This is a data iteration workflow used to iterate throug query update sets.

Created: 2012-01-29

Credits: Matej Mihelčić Matko Bošnjak

Model testing workflow (1)

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This workflow measures performance of three models. Model learned on train data and upgraded using online model updates. Model learned on train data + all query update sets. Model learned on train data only.

Created: 2012-01-29

Credits: Matej Mihelčić

Model saving workflow (1)

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This workflow trains and saves a model for a selected item recommendation operator.

Created: 2012-01-29 | Last updated: 2012-01-30

Credits: Matej Mihelčić

Recommender workflow (1)

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This is a main online update experimentation workflow. It consists of three Execute Process operators. First operator executes model training workflow. Second operator executes online updates workflow for multiple query update sets. The last operator executes performance testing and comparison workflow. Final performance results are saved in an Excel file.

Created: 2012-01-29

Credits: Matej Mihelčić

Operator testing workflow (1)

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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 Recommend...

Created: 2012-01-29

Credits: Matej Mihelčić Matko Bošnjak

Metafeature extraction (1)

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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.

Created: 2012-01-29 | Last updated: 2012-01-30

Credits: Matko Bošnjak

Iterate through datasets (1)

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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 t...

Created: 2012-01-29

Credits: Matej Mihelčić Matko Bošnjak