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Experimental user to item score matrix Excel file

Created: 2011-11-26 20:07:02 | Last updated: 2011-11-26 20:07:04

Credits: Matko Bošnjak

License: Creative Commons Attribution-Share Alike 3.0 Unported License

File type: Excel workbook

Comments: 0 | Viewed: 327 times | Downloaded: 203 times

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Rating prediction query datasets

Created: 2012-01-29 17:05:46 | Last updated: 2012-01-29 17:07:49

Credits: tomS

License: Creative Commons Attribution-Share Alike 3.0 Unported License

File type: application/x-zip-compressed

Comments: 0 | Viewed: 63 times | Downloaded: 46 times

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Item recommendation query datasets

Created: 2012-01-29 17:02:50 | Last updated: 2012-01-30 13:20:16

Credits: tomS

License: Creative Commons Attribution-Share Alike 3.0 Unported License

File type: application/x-zip-compressed

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Help for using experimentation workflows with recomm...

Created: 2012-01-29 11:59:49 | Last updated: 2012-01-29 17:28:20

Credits: Matej Mihelčić

License: Creative Commons Attribution-Share Alike 3.0 Unported License

File type: application/publicationlist

Comments: 0 | Viewed: 63 times | Downloaded: 55 times

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Data example for experimentation with recommender ex...

Created: 2012-01-29 11:53:41 | Last updated: 2012-01-30 13:19:23

Credits: tomS

License: Creative Commons Attribution-Share Alike 3.0 Unported License

File type: application/x-zip-compressed

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MovieLens data for hybrid recommenders

Created: 2012-05-17 11:04:57 | Last updated: 2012-05-17 11:06:10

Credits: tomS

License: Creative Commons Attribution-Share Alike 3.0 Unported License

File type: Trident (Package)

Comments: 0 | Viewed: 175 times | Downloaded: 145 times

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e-LICO Recommender Systems

Unique name: eLICORS
Created: Friday 27 January 2012 13:10:41 (UTC)

28 shared items   |   0 announcements

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Online update experiment pack

Created: 2012-01-29 16:29:09 | Last updated: 2012-01-29 22:06:46

12 items in this pack

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Experimentation for recommender extension templates

Created: 2012-01-28 21:54:16 | Last updated: 2012-01-31 16:01:43

6 items in this pack

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Hybrid recommender RapidMiner and extension operators

Created: 2012-05-17 10:57:03 | Last updated: 2012-05-17 11:13:51

2 items in this pack

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Random recommender (1)

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This process does a random item recommendation; for a given item ID, from the example set of items, it randomly recommends a desired number of items. The purpose of this workflow is to produce a random recommendation baseline for comparison with different recommendation solutions, on different retrieval measures. The inputs to the process are context defined macros: %{id} defines an item ID for which we would like to obtain recommendation and %{recommender_no} defines the required number of ...

Created: 2011-03-15 | Last updated: 2011-03-15

Collaborative filtering recommender (1)

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This process executes a collaborative filtering recommender based on user to item score matrix. This recommender predicts one user’s score on some of his non scored items based on similarity with other users. The inputs to the process are context defined macros: %{id} defines an item ID for which we would like to obtain recommendation and %{recommender_no} defines the required number of recommendations and %{number_of_neighbors} defines the number of the most similar users taken into a...

Created: 2011-03-15 | Last updated: 2012-03-06

Content based recommender (1)

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This process is a special case of the item to item similarity matrix based recommender where the item to item similarity is calculated as cosine similarity over TF-IDF word vectors obtained from the textual analysis over all the available textual data. The inputs to the process are context defined macros: %{id} defines an item ID for which we would like to obtain recommendation and %{recommender_no} defines the required number of recommendations. The process internally uses an example set of...

Created: 2011-03-15 | Last updated: 2011-03-15

Item to item similarity matrix -based reco... (1)

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This process executes the recommendation based on item to item similarity matrix. The inputs to the process are context defined macros: %{id} defines an item ID for which we would like to obtain recommendation and %{recommender_no} defines the required number of recommendations. The process internally uses an item to item similarity matrix written in pairwise form (id1, id2, similarity). The process essentially filters out appearances of the required ID in both of the columns of the pairwis...

Created: 2011-03-15 | Last updated: 2011-03-15

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

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

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

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

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

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