Advanced Recommender Systems

This module provides an overview of advanced collaborative filtering, delving into item-based collaborative filtering as an optimization problem and exploring SLIM, Bayesian Probabilistic Ranking, and more. The module concludes with a comprehensive course syllabus, acknowledgments, and a graded assessment.

Explore singular value decomposition (SVD) techniques in this module, covering matrix factorization, Funk SVD, SVD++, Asymmetric SVD, and Pure SVD. Additionally, the module delves into explaining the model and explores recommendation items and explainability in machine learning, followed by a graded assessment.

This module focuses on hybrid and context-aware recommender systems, covering linear and list combinations, pipelining, merging models, collaborative filtering with side information, and context-aware recommender systems. It concludes with a graded assessment covering tensor-based factorization, preferences in context, and a matter of weights.

Delve into factorization machines in this module, exploring the core concepts, extending the model, and solving imbalance problems. The module concludes with a graded assessment covering factorization machines and multimedia contents.

This honors module presents the Recsys Challenge, offering an opportunity to apply the knowledge and skills gained throughout the course to real-world challenges in recommender system development.