Recommender Systems

Related lectures (25)

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Covers unsupervised learning for movie recommendation using singular value decomposition.

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.

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Explores text classification using Naïve Bayes in content-based recommenders.

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

Explores LU decomposition for matrix factorization and solving linear systems.

Covers the basics of HCI, emphasizing usability, playfulness, and the importance of professional training in design.

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.