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

A hybrid recommendation framework that integrates multi-matrix factorization and granular sentiment analysis to deliver explainable user-item matching. By jointly modeling user preferences and feature ...

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 ...

When The Matrix premiered in 1999, the film not only changed movies forever, it changed the way people saw the world around them. Now, more than 25 years later, Cosm has partnered with Warner Bros.

Matrix factorization techniques, such as principal component analysis (PCA) and independent component analysis (ICA), are widely used to extract geological processes from geochemical data. However, ...

Abstract: In this paper, we propose a new low-rank matrix factorization model, dubbed bounded simplex-structured matrix factorization (BSSMF). Given an input matrix X and a factorization rank r, BSSMF ...