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Algorithms for non-negative matrix factorization

D Seung, +1 more
- Vol. 13, pp 556-562
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The article was published on 2001-01-01 and is currently open access. It has received 5015 citations till now. The article focuses on the topics: Non-negative matrix factorization.

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Fused matrix factorization with multi-tag, social and geographical influences for POI recommendation

TL;DR: The muti-tag, social and geographical influences separately form three layers of LBSN and fuse the social regularization, geographical influence into a famous matrix factorization(MF) framework that outperforms other state-of-the-art approaches on recommendation.
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Discovering social spammers from multiple views

TL;DR: This work proposes a generalized social spammer detection framework by jointly integrating multiple view information and a novel social regularization term into a classification model, and introduces a simple strategy to fill the missing data for each view.
Journal ArticleDOI

Fast Decomposition of Large Nonnegative Tensors

TL;DR: Following recent works on HOSVD compression for Big Data, this work detail solutions to decompose a nonnegative tensor into decomposable terms in a compressed domain.
Journal ArticleDOI

Randomized Nonnegative Matrix Factorization

TL;DR: This paper presents a randomized hierarchical alternating least squares (HALS) algorithm to compute the NMF by deriving a smaller matrix from the nonnegative input data, so that a more efficient nonnegative decomposition can be computed.
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