Abstract
Collaborative filtering is a technique for reducing information overload and the task is to predict
missing values in a users vs. items matrix. GroupLens uses the weighted averages of ratings given by the
"neighbors" considering similarities to the active user. It is also pointed out that the performance can be
improved by item-based approach considering similarities among items. This paper proposes a linear fuzzy
clustering model based on item partitioning and apply it to an item-based collaborative filtering system.
Experimental results demonstrate that the model can be used for revealing mutual relation among variables
and the item-based approach is useful for improving the performance of the model-based prediction model.
