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Modifications for non-standard situations (e.g., Erasmus students) are possible, but have to be discussed and arranged with the instructor.
The course aims at presenting the main and most important concepts related to recommendation systems.
Program:
– introduction
– history ed evolution of recommendation systems
– neighborhood based collaborative filtering
– model-based collaborative filtering
– content based methods
– knowledge based methods
– hybrid methods
– evaluation
– context based methods
– structural methods
– advanced topics (learning to rank, multi-harmed bandits, graph models, neural models, counterfactual evaluation)
– case studies and specific topics
– “Recommender Systems Handbook” – https://link.springer.com/book/10.1007/978-0-387-85820-3
– “Practical Recommender Systems” – https://www.manning.com/books/practical-recommender-systems
– “Recommender Systems: An Introduction” – https://www.amazon.it/Recommender-Systems-Introduction-Dietmar-Jannach/dp/0521493366
– “Dive into Deep Learning” – https://d2l.ai/
– additional material presented by the instructor
