Supervision: Gjergji Kasneci, Maximilian Jenders
This seminar will give an overview of state-of-the-art recommendation techniques and methods for dealing with large and sparse user-item matrices. It will be held in English and will consist of three parts.
In the first part of the seminar the students will be introduced to the above mentioned techniques. The main material will consist of work (in form of scientific papers).
In the second part, the students will form groups; each group will be given the opportunity to implement basic data structures for scalable recommendation techniques.
Finally, in the third part, the students will implement recommendation algorithms that build on such data structures. The algorithms will be evaluated on a large-scale dataset extracted from various social news sites.
At the end of the semester, each team will deliver a brief 2-4 page summary of the pursued project.