Recommendation

Explains the rating matrix, adjacency matrix, degree matrix, and Laplacian matrix used in recommender systems, computing them with Python's Networkx library.

Explains SVD and PCA fundamentals with NumPy code examples, and how SVD enables low-rank approximation for recommender system rating matrices.

Explains the definition and computation of the pseudo-inverse for matrices that lack a true inverse due to rank deficiency, with a Python example.

Explains evaluation methods for recommender systems, from accuracy metrics like precision and recall to offline and online evaluation, with Python examples.

Explains the formulation and optimization problem of matrix factorization in recommender systems, with an SVD implementation using movielens-100k.

Introduces the Surprise Python library for building recommender systems, covering its features and a matrix-factorization implementation example.

Explains how to use the implicit recommender library, covering the ALS and BPR algorithms with a movielens-100k dataset example.