Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Effects of Singular Value Decomposition on Collaborative Filtering
The Effects of Singular Value Decomposition on Collaborative Filtering
SVD-based collaborative filtering with privacy
Proceedings of the 2005 ACM symposium on Applied computing
On the Low-Rank Approximation of Data on the Unit Sphere
SIAM Journal on Matrix Analysis and Applications
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Collaborative filtering is a recent technique that recommends products to customers using other users' preference data. The performance of a collaborative filtering system generally degrades when the number of customers and products increases, hence the dimensionality of filtering database needs to be reduced. In this paper, we discuss the use of weighted low rank matrix approximation to reduce the dimensionality of a partially known dataset in a collaborative filtering system. Particularly, we introduce a projected gradient flow approach to compute a weighted low rank approximation of the dataset matrix.