Matrix Factorization: Difference between revisions

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== Parameters ==  
== Parameters ==  


No parameters found.
$n$: dimension of matrix


== Table of Algorithms ==  
== Table of Algorithms ==  

Revision as of 08:23, 10 April 2023

Description

Collaborative filtering is a technique used in recommendation systems. It analyzes relationships between users and interdependencies among products to identify new user-item associations.

A method of collaborative filtering uses matrix factorization. In its basic form, matrix factorization characterizes both items and users by vectors of factors inferred from item rating patterns.

Parameters

$n$: dimension of matrix

Table of Algorithms

Name Year Time Space Approximation Factor Model Reference
LU Matrix Decomposition 1945 $O(n^{3})$ $O(n^{2})$ Exact Deterministic
QR Matrix Decomposition 1955 $O(n^{2})$ $O(n^{2})$ Exact Deterministic
Cholesky Decomposition 1983 $O(n^{2})$ $O(n^{2})$ Exact Deterministic

Time Complexity Graph

Collaborative Filtering - Matrix Factorization - Time.png

Space Complexity Graph

Collaborative Filtering - Matrix Factorization - Space.png

Time-Space Tradeoff

Collaborative Filtering - Matrix Factorization - Pareto Frontier.png