GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Clustering Algorithm Extracting Principal Components Independent of Subsidiary Variables
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Improving the Quality of the Personalized Electronic Program Guide
User Modeling and User-Adapted Interaction
Quantification of multivariate categorical data considering clusters of items and individuals
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
A comparison of clustering-based privacy-preserving collaborative filtering schemes
Applied Soft Computing
An entropy-based neighbor selection approach for collaborative filtering
Knowledge-Based Systems
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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.