A perceptual approach to user clustering in collaborative filtering

This paper considers a new approach to user clustering in collaborative filtering. Collaborative filtering is a technique for reducing information overload and is achieved by predicting the applicability of items to user. In neighborhood-based algorithms, the applicability is given by the weighted averages of ratings of neighbors. The new clustering method plays a role for selecting the neighbors based on a perceptual approach, in which users and items are partitioned into two clusters by balancing a general signed graph composed of alternative evaluations on items by users.