Advanced Recommendation Techniques (Sommersemester 2013)
- Weekly Hours: 4
- Credits: 6
- Enrolment Deadline: 10.2.2013 - 30.4.2013
- Teaching Form: SP
- Enrolment Type: Compulsory Elective Module
Programs & Modules
- Business Process & Enterprise Technologies
- Operating Systems & Information Systems Technology
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.
The grading will approximately follow the distribution below:
- 25% for presentations
- 40% implementation
- 20% project report
- 15% participation in feedback and progress meetings
The seminar will start with the beginning of the 2013 summer semester on Mo., April 8th.
In the first two weeks, the students will have the opportunity to study related research articles, which will be discussed in the seminar meetings (Mondays and Wednesdays).
By the end of the second week the students will form two person teams; each of the teams should decide which topic it would like to work on. Once the students have selected their favorite topics, there will be only weekly or biweekly feedback and progress meetings with individual teams.
There will also be one intermediate and an end presentation from each group.