Collaborative filtering, content-based filtering, hybrid methods, and personalization algorithms.
- Google: Recommendation Systems Course - Comprehensive guide to building recommendation systems from Google.
Beginner - Coursera: Recommender Systems Specialization (Minnesota) - In-depth specialization covering collaborative filtering, content-based, and hybrid methods.
Intermediate - Stanford CS246: Mining Massive Datasets (RecSys) - Recommendation systems within the context of large-scale data mining.
Intermediate - fast.ai: Collaborative Filtering - Practical deep learning with collaborative filtering examples.
Beginner
- Recommender Systems Handbook (Ricci et al.) - Comprehensive reference on recommender system techniques.
Advanced - Recommender Systems: The Textbook (Aggarwal) - Broad coverage of recommendation algorithms and techniques.
Intermediate - Netflix Prize Papers - Historical resources from the competition that advanced the field.
Intermediate
- Microsoft Recommenders (GitHub) - Best practices and reference implementations for recommendation systems.
Intermediate - Surprise Library - Python scikit for building and analyzing recommender systems.
Beginner - LensKit - Open-source toolkit for building and evaluating recommender systems.
Intermediate - RecBole - Unified framework for developing and reproducing recommendation algorithms.
Intermediate - ACM RecSys Resources - Conference resources and educational materials.
All Levels - Merlin (NVIDIA) - GPU-accelerated framework for large-scale recommendation systems.
Advanced