Recommender Systems: Methods and Applications
A review of your exam sheets (2nd attempt) will be possible on 12.10.16 at 2 p.m. in the room G29-021.
|Vorlesung(V) - Lecture - Dates/Times/Location:|
|Mon.||15:00 bis 17:00||weekly||G22A-112 (40 Pl./16 Pl.CoV19)||Spiliopoulou||40|
|Übung (Ü) - Exercise - Dates/Times/Location:|
|Wed.||15:00 bis 17:00||weekly||G22A-120 (40 Pl./16 Pl.CoV19)||Matuszyk||65|
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|
- Block 1a - Setting the Scene
- Block 1b - Design
- Block 2a - RecSys Basics
- Block 2b - Evaluation
- Block 3 - RecSys Advanced
- Block 2c - Model-based Methods (updated 19.01.16)
- Exercise sheet 1
- Exercise sheet 2
- Exercise sheet 3
- Envoronment models - slides
- Exercise sheet 4
- Exercise sheet 5 (udated 14.01.16)
- Slides - Matrix Factorization (part 1 + part 2)