Recommender Systems: Methods and Applications
The last chance to review the exam, for those who missed the first appointment, is on 10.05.17 at 3 p.m. in the room G29-130.
We offer a possibility to review your exam on 11.04.17 at 11 a.m. in the room G29-135 (professor's office).
|Vorlesung(V) - Lecture - Dates/Times/Location:|
|Tue.||15:00 bis 17:00||weekly||G29-307 (120 Pl./42 Pl. CoV19)||Spiliopoulou||60|
|Übung (Ü) - Exercise - Dates/Times/Location:|
|Tue.||09:00 bis 11:00||weekly||G22A-112 (40 Pl./16 Pl.CoV19)||Matuszyk||40|
Overview (from LSF)
In this course we elaborate on the role of recommenders as a primary means of improving a user's or customer's experience, while increasing company revenue. The course covers learning methods for the recommender core, approaches for the design and evaluation of recommenders, and specific application areas of recommenders.
Recommender design and evaluation
Background in data mining is of advantage. This course is also appropriate for students who have heard the CRM/RecSys bachelor course.
|Description||Recommender Systems: Methods and Applications|
- Administrative matters
- 1a Setting The Scene
- 1b Design
- 2a Basics (updated on 15.11.16)
- 2b Evaluation
- 3 Dealing with Big Issues
- 3c Opinion Mining
- Exercise sheet 1
- Exercise sheet 2
- Environment models - slides
- Exercise sheet 3 (updated on 30.11.16)
- Exercise sheet 4
- Exercise sheet 5
- Exercise slides - matrix factorization