Recommender Systems: Part 1

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Related lectures (24)

Page 1 of 3

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

Optimization in Machine Learning

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

Matrix Factorization: Optimization and Evaluation

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

Recommender Systems: Matrix Factorization & Evaluation

Explores matrix factorization techniques for recommender systems, including evaluation metrics like RMSE and NDCG.

Recommender Systems

Explores the evolution and impact of recommender systems, covering information retrieval, collaborative filtering, and different recommendation algorithms.

Stochastic Optimization and Adaptive Gradient Methods

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

Linear Algebra Review

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

Recommender Systems: Basics and Techniques

Covers collaborative filtering and content-based methods for recommender systems, addressing cold start problems and making predictions.

Recommender Systems: MovieLens Dataset

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

Recommender Systems: Personalized Recommendations for Users

Delves into challenges of content personalization, focusing on web recommendations and personalized newsletters.