Recommender Systems: Matrix Factorization & Evaluation

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Related lectures (28)

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Matrix Factorization: Optimization and Evaluation

Explores matrix factorization optimization, evaluation methods, and challenges in recommendation systems.

Recommender Systems: Matrix Factorization

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

Linear Systems: Chapters 4, 5, 6

Explores the link between linear systems and optimization through elimination and LU decomposition.

Optimization in Machine Learning

Explores optimization techniques, word embeddings, and recommendation systems in machine learning.

Recommender Systems: MovieLens Dataset

Covers implementing recommender systems using the MovieLens dataset and evaluating them with RMSE and MAE metrics.

Linear Algebra Review

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

Recommender Systems

Explores recommender systems, collaborative filtering, content-based recommendations, similarity metrics, and advanced methods like matrix factorization.

Characterization of Invertible Matrices

Explores the properties of invertible matrices, including unique solutions and linear independence.

Determinant, Area, and Volume

Explores area determination, linear applications, matrix operations, and vector spaces.

Singular Value Decomposition: Image Compression and Applications

Covers Singular Value Decomposition, focusing on its application in image compression and data representation.