Abstract
Collaborative filtering is a technique for reducing information overload, and personalized recommendation is performed by predicting missing values in a data matrix. Linear fuzzy clustering is a technique for local principal component analysis and can be used for constructing local prediction models considering data substructures. This paper proposes a new algorithm for constructing local linear models that performs a simultaneous application of fuzzy clustering and principal component analysis based on sequential subspace learning. In numerical experiments, the diagnostic power of the filtering system is shown to be improved by predicting missing values using the proposed local linear models.
