Description:

 Here we provide four workflows that build the recommender engine by using the recommender extension.

The Recommender Extension can be downloaded from the Rapid-I Marketplace from: http://rapidupdate.de:8180/UpdateServer/faces/product_details.xhtml?productId=rmx_irbrecommender .

More details can be found: http://elico.rapid-i.com/recommender-extension.html

Description:

This workflow provides transformation of an item description attribute set from RMonto operator into a format required by attribute based k-NN operators of the Recommender extension.

Description:

  This dataset is used in:

Description:

This workflow takes input 2 as as train set for several item recommendation predictions. We build four different recommendation model which are combined into one model with operator Model Combiner. Then we take input 1 as a test set and apply merged model. Train and test set must contain user_id, item_id and rating attributes which need to have special roles user identification, item identification and label. This workflow uses recommender system extension.

Description:

 This dataset is used in:

Description:

This workflow takes input 2 as a train set for recommender systems. We build two item recommendation models: item k-NN (collaborative based) and item attribute k-NN (content based). Item attribute k-NN operator takes additional item attributes from input 3. We combine two models with operator model combiner and test performance on test set (input 1). Train and test set must contain user_id and item_id attributes which need to have special roles user identification and item identification. This workflow uses recommender system extension.

Description:

 This dataset is used in:

Pack: Recommender systems workflow templates 2012

http://www.myexperiment.org/packs/238.html.

This is a sample dataset of the Yahoo! Music community's preferences to various musical items, 

Description:

This workflow takes input 2 as a train set for item-k-NN recommender system. The model peformance is evaluated on test set (input 1). This workflow uses recommender system extension. Train and test set must contain user_id and item_id atrributes which need to have special roles user identification and item identification.