LinkedIn

How do you compare matrix factorization with other collaborative filtering techniques?

Matrix factorization is a powerful technique for building recommender systems that can predict user preferences and ratings for items. In this tutorial, you will learn the basics of matrix ...
IEEE

Putting nonnegative matrix factorization to the test: a tutorial derivation of pertinent cramer—rao bounds and performance benchmarking

Abstract: Nonnegative matrix factorization (NMF) is a useful tool in a broad range of applications, from signal separation to computer vision and machine learning. NMF is a hard (NP-hard) ...
GitHub

mlpack_layers /doc /tutorials

Alternating matrix factorization decomposes a matrix V in the form V ~ WH where W is called the basis matrix and H is called the encoding matrix. V is taken to be of size n x m and the obtained W is n ...
GitHub

mlpack-linkinpony /doc /tutorials

Alternating matrix factorization decomposes a matrix V in the form V ~ WH where W is called the basis matrix and H is called the encoding matrix. V is taken to be of size n x m and the obtained W is n ...
Frontiers

Collaborative filtering based on nonnegative/binary matrix factorization

Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items. To predict scores for unrated items, matrix ...
IEEE

Semi-Supervised Symmetric Non-Negative Matrix Factorization With Low-Rank Tensor Representation

Abstract: Semi-supervised symmetric non-negative matrix factorization (SNMF) utilizes the available supervisory information (usually in the form of pairwise constraints) to improve the clustering ...
Nature

Hyperbolic matrix factorization improves prediction of drug-target associations

Past research in computational systems biology has focused more on the development and applications of advanced statistical and numerical optimization techniques and much less on understanding the ...