Following Lekakos and Giaglis ( 2007 ) , one approach for achieving this objective consists of applying supervised learning , where a winning method is selected for each case in the training set , all the training cases are labeled accordingly , and then the system is trained to predict a winner for unseen cases .
a hybrid approach for improving predictive accuracy of collaborative filtering algorithms user modeling and useradapted interaction
G Lekakos G M Giaglis
These predictions enable our system to recommend a particular method for handling a new ( unseen ) request ( Marom , Zukerman , and Japkowicz 2007 ) . Following Lekakos and Giaglis ( 2007 ) , one approach for achieving this objective consists of applying supervised learning , where a winning method is selected for each case in the training set , all the training cases are labeled accordingly , and then the system is trained to predict a winner for unseen cases . However , in our situation , there is not always one single winner ( two methods can perform similarly well for a given request ) , and there are different ways to pick winners ( for example , based on F-score or precision ) .
Following @@CITATION , one approach for achieving this objective consists of applying supervised learning , where a winning method is selected for each case in the training set , all the training cases are labeled accordingly , and then the system is trained to predict a winner for unseen cases .