AN ENSEMBLE MODEL FOR COLLABORATIVE FILTERING TO INVOLVE ALL ASPECTS OF DATASET

Year 2018, Volume: 60 Issue: 2, 15 - 26, 01.08.2018

YILMAZ Ar

Abstract

The accuracy of
predictions is better if the combinations of the different approaches are used.
Currently in collaborative filtering research, the linear blending of various
methods is used. More accurate classifiers can be obtained by combining less
accurate ones. This approach is called ensembles of classifiers. Different
collaborative filtering methods uncover the different aspects of the dataset.
Some of them are good at finding out local relationships; the others work for
the global characterization of the data. Ensembles of different collaborative
filtering algorithms can be created to provide more accurate recommender
systems.

Keywords

Collaborative filtering, ensemble methods, negative correlation learning

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