recommender systems

Summarizes the basic theory and Python implementation of GNNs (Graph Neural Networks) and GCNs (Graph Convolutional Networks), a specific type of GNN.

Explains why spatial convolution and frequency-domain element-wise multiplication are equivalent in graph signal processing, with theory and Python examples for GNNs.

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.

Explains content-based collaborative filtering and how cosine similarity between feature vectors drives recommendations, with a Python example.

Explains user-based and item-based memory-based collaborative filtering via cosine similarity and Pearson correlation, with a Python example.

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.