Matrix Factorization: Difference between revisions
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Latest revision as of 10:10, 28 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
