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; 3rd edition; 2022.
• Recommender Systems: The Textbook; Charu C. Aggarwal; Springer; 2016.

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

Allocation of places in courses with a limited number of participants (PS, SE, VU, PJ)

In courses with a limited number of participants, course places are allocated as follows:

1. Students for whom the study duration would be extended due to the postponement are to be given priority.

2. If the criteria in no. 1 do not suffice, first, students for whom this course is part of a compulsory module are to be given priority, and second, students for whom this course is part of an elective module.

3. If the criteria in no. 1 and 2 do not suffice, the available places are drawn by random.

Curriculum MA Computer Science 2021W