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

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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