Collaborative Filtering Using Principal Component Analysis and Fuzzy Clustering

Automated collaborative filtering is a popular technique for reducing information overload. In this paper, we propose a new approach for the collaborative filtering using local principal components. The new method is based on a simultaneous approach to principal component analysis and fuzzy clustering with an incomplete data set including missing values. In the simultaneous approach, we extract local principal components by using lower rank approximation of the data matrix. The missing values are predicted using the approximation of the data matrix. In numerical experiment, we apply the proposed technique to the recommendation system of background designs of stationery for word processor.