Linear Fuzzy Clustering Based on Variable Partitioning and Its Application to Collaborative Filtering

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.