Recommender Systems: Matrix Factorization & Evaluation

Related lectures (28)

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Covers linear systems, diagonal and triangular matrices, and LU factorization.

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Covers the resolution of linear systems and its link to optimization problems.

Introduces recommender systems, collaborative filtering, content-based recommendation, similarity metrics, and matrix factorization.

Explores LU decomposition for matrix factorization and solving linear systems.

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

Covers semi-definite programming and optimization over positive semidefinite cones.

Explores stochastic optimization, adaptive gradient methods, recommender systems, and matrix factorization in user-item rating matrices.

Explores differentiable ranking and sorting techniques for machine learning applications.

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