抄録
Automated collaborative filtering is a computational realization of ``word-of-mouth'' in network community and the applicability of each item for users is predicted based on missing values estimation in a matrix of users versus items.
The original memory-based system of GroupLens uses the weighted averages of ratings given by the ``neighbors'' considering similarities to the active user.
A similar idea was applied to the model-based system based on linear fuzzy clustering, in which missing values are predicted considering local substructures.
This paper considers combining the numerical evaluation matrix with other categorical information and proposes a collaborative filtering system based on linear fuzzy clustering with nominal variable quantification.
Numerical experiments demonstrate that categorical information is useful for improving the performance of the model-based prediction model.
