Abstract: Matrix factorization is a central paradigm in matrix completion and collaborative filtering. Low-rank factorizations have been extremely successful in reconstructing and generalizing ...

Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the ...

Abstract: A non-negative matrix factorization (NMF) is effectively applied to analyze data in an unsupervised way. Though non-negative factors are endowed with favorable interpretability, such as part ...

Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States ...

One scene reflects the themes — A.I., fake news, transgender lives and Gen X — that make the film a classic. By Alissa Wilkinson Neo, the hero of “The Matrix,” is sure he lives in 1999. He has a green ...

Implemented the Alternating Least Squares (ALS) algorithm to factorize the interaction matrix into user and item latent factors, considering both interaction strength and confidence.

Python is convenient and flexible, yet notably slower than other languages for raw computational speed. The Python ecosystem has compensated with tools that make crunching numbers at scale in Python ...

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on an implementation of the technique that emphasizes simplicity and ease-of-modification over robustness and ...