A Sequential Learning Algorithm for Collaborative Filtering With Linear Fuzzy Clustering
Katsuhiro Honda, Hidetomo Ichihashi, Akira Notsu · 2006
Collaborative filtering is a technique for reducing information overload, and personalized recommendation is performed by predicting missing values in a data matrix. While the memory-based algorithms are widely used in conjunction with Web technology, the model-based algorithms are useful for estimating prediction models, in which we can predict missing values without holding all elements of the data matrix. Linear fuzzy clustering is a technique for local principal component analysis and can be used for estimating local prediction models considering data substructures. This paper proposes a new algorithm for estimating 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.