LFU:online

Graduates of this course have obtained an advances understanding in the field of recommender systems and have acquired the ability to  independently educate themselves in the field.

Foundations of recommender systems; non-personalized recommendations; collaborative filtering; content-based filtering; hybrid recommender systems; matrix factorization; advanced techniques for recommender systems; evaluation of recommender systems

Interactive lecture; practical exercises and projects; discussion of state-of-the-art research papers

written exam and student presentations

• Practical Recommender Systems; Kim Falk; Manning Publications; 2019.
• Recommender Systems Handbook; Francesco Ricci, Lior Rokach,  Bracha Shapira; Springer; 2nd edition; 2015.
• Recommender Systems: The Textbook; Charu C. Aggarwal; Springer; 2016.

 Further resources are available on OLAT and in the lecture slides.

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