Membership enhancement with exponential fuzzy clustering for collaborative filtering

Abstract

In Recommendation System, Collaborative Filtering by Clustering is a technique to predict interesting items from users with similar preferences. However, misleading prediction could be taken place by items with very rare ratings. These missing data could be considered as noise and influence the cluster's centroid by shifting its position. To overcome this issue, we proposed a new novel fuzzy algorithm that formulated objective function with Exponential equation (XFCM) in order to enhance ability to assign degree of membership. XFCM embeds noise filtering and produces membership for noisy data differently to other Fuzzy Clustering. Thus the centroid is robust in the noisy environment. The experiments on Collaborative Filtering dataset show that centroid produced by XFCM is robust by the improvement of prediction accuracy 6.12% over Fuzzy C-Mean (FCM) and 9.14% over Entropy based Fuzzy C-Mean (FCME). © 2010 Springer-Verlag.