Matrix factorization techniques have become pivotal in data mining, enabling the extraction of latent structures from large-scale data matrices. These methods decompose complex datasets into ...

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 ...

Nonnegative Matrix Factorization (NMF) has emerged as a powerful tool in data analysis, particularly noted for its ability to produce parts‐based, interpretable representations from high-dimensional ...

This is a preview. Log in through your library . Abstract Matrix factorization in numerical linear algebra (NLA) typically serves the purpose of restating some given problem in such a way that it can ...

Abstract: Integration of multi-task brain imaging data is crucial for furthering our understanding of neurodevelopment. By utilizing functional magnetic resonance imaging (fMRI) data and machine ...

This repository contains my implementation for Task 4 of the internship, which focuses on building a Recommendation System using either Collaborative Filtering or Matrix Factorization techniques. The ...

DESCRIPTION: As part of my internship, Task 4 involved the development of a Recommendation System using Collaborative Filtering and Matrix Factorization techniques. Recommendation systems are among ...

This is a preview. Log in through your library . Abstract Many-electron wave functions are usually constructed from antisymmetrized products of one-electron orbitals (determinants) and energy ...