Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Weighted Nonnegative Matrix Co-Tri-Factorization for Collaborative Prediction
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Community discovery using nonnegative matrix factorization
Data Mining and Knowledge Discovery
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Collaborative filtering aims at predicting a test user's ratings for new items by integrating other like-minded users' rating information. Traditional collaborative filter- ing methods usually suffer from two fundamental problems: sparsity and scalability. In this paper, we propose a novel framework for collaborative filtering by applying Orthogo- nal Nonnegative Matrix Tri-Factorization (ONMTF), which (1) alleviates the sparsity problem via matrix factorization; (2)solves the scalability problem by simultaneously cluster- ing rows and columns of the user-item matrix. Experimental results on benchmark data sets are presented to show that our algorithm is indeed more tolerant against both spar- sity and scalability, and achieves good performance in the meanwhile.