Recommender Systems: Part 1

Related lectures (24)

Page 2 of 3

Explores matrix factorization in recommender systems, covering optimization, evaluation metrics, and challenges in scaling.

Explores the evolution of recommenders, collaborative filtering, Netflix Prize, model training, and optimization techniques.

Introduces LU decomposition for efficient linear equation solving using matrix factorization.

Explores recommender systems, latent factor models, and clustering algorithms for structure discovery.

Explores LU decomposition for matrix factorization and solving linear systems.

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

Explores direct methods for solving linear equations and the impact of errors on solutions and matrix properties.

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

Explores matrix inversion, conditions for invertibility, uniqueness of the inverse, and elementary matrices for inversion.