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» Computing with abstract matrix structures
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ICDM
2009
IEEE
202views Data Mining» more  ICDM 2009»
14 years 7 months ago
Link Prediction on Evolving Data Using Matrix and Tensor Factorizations
Abstract--The data in many disciplines such as social networks, web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In t...
Evrim Acar, Daniel M. Dunlavy, Tamara G. Kolda
AAECC
2007
Springer
111views Algorithms» more  AAECC 2007»
14 years 10 months ago
When cache blocking of sparse matrix vector multiply works and why
Abstract. We present new performance models and a new, more compact data structure for cache blocking when applied to the sparse matrixvector multiply (SpM×V) operation, y ← y +...
Rajesh Nishtala, Richard W. Vuduc, James Demmel, K...
ECWEB
2009
Springer
204views ECommerce» more  ECWEB 2009»
15 years 4 months ago
Computational Complexity Reduction for Factorization-Based Collaborative Filtering Algorithms
Abstract. Alternating least squares (ALS) is a powerful matrix factorization (MF) algorithm for both implicit and explicit feedback based recommender systems. We show that by using...
István Pilászy, Domonkos Tikk
ICPR
2008
IEEE
15 years 11 months ago
A matrix alignment approach for link prediction
This paper introduces a new discriminative learning technique for link prediction based on the matrix alignment approach. Our algorithm automatically determines the most predictiv...
Jerry Scripps, Pang-Ning Tan, Feilong Chen, Abdol-...
ML
2008
ACM
146views Machine Learning» more  ML 2008»
14 years 9 months ago
Improving maximum margin matrix factorization
Abstract. Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful lear...
Markus Weimer, Alexandros Karatzoglou, Alex J. Smo...