Analysis of a low-dimensional linear model under recommendation attacks
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Robust collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Open Domain Recommendation: Social Networks and Collaborative Filtering
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Learning Bidirectional Similarity for Collaborative Filtering
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
User credit-based collaborative filtering
Expert Systems with Applications: An International Journal
An effective algorithm for dimensional reduction in collaborative filtering
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
Learning bidirectional asymmetric similarity for collaborative filtering via matrix factorization
Data Mining and Knowledge Discovery
An improved privacy-preserving DWT-based collaborative filtering scheme
Expert Systems with Applications: An International Journal
Knowledge-Based Systems
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Singular Value Decomposition (SVD), together with the Expectation-Maximization (EM) procedure, can be used to find a low-dimension model that maximizes the log-likelihood of observed ratings in recommendation systems. However, the computational cost of this approach is a major concern, since each iteration of the EM algorithm requires a new SVD computation. We present a novel algorithm that incorporates SVD approximation into the EM procedure to reduce the overall computational cost while maintaining accurate predictions. Furthermore, we propose a new framework for collaborating filtering in distributed recommendation systems that allows users to maintain their own rating profiles for privacy. A server periodically collects aggregate information from those users that are online to provide predictions for all users. Both theoretical analysis and experimental results show that this framework is effective and achieves almost the same prediction performance as that of centralized systems.