Difference between revisions of "TU Wien:Recommender Systems VU (Sacharidis)"
(Added content, required knowledge and critics)
m (Berti933 verschob die Seite TU Wien:Recommender Systems VU (Dimitrios) nach TU Wien:Recommender Systems VU (Sacharidis): first name -> last name)
Revision as of 13:11, 22 June 2020
|Links||tiss:194035 , Mattermost-Channel|
|Master Data Science||Pflichtmodul MLS/CO - Machine Learning and Statistics - Core|
|Master Business Informatics||Pflichtmodul EE/COR|
Various recommendation systems are introduced in a structurally clear and understandable manner. Students learn to understand basic principles of recommending items to users with focus on rating-based data (items rated f.e. on scale 1-10, rather than simply likes).
The lecture starts by explaining user-user collaborative filtering (CF), goes on to item-item CF, then introduces matrix factorisation methods, explore more advanced recommendation methods, such as factorisation machines, and finally describes evaluation and performance metrices.
Weekly lecture, 4 assignments to be done individually, a group project and a final exam
simple matrix operations and mathematical optimisation
experience with python and the numpy and pandas modules
4 assignments to be solved individually, 1 project in groups of 5+ people.
- Assignment 1: Implement a User-User recommender system, important code already predefined, such that student only needs care about the algorithmic part of the problem
- Assignment 2: Implement a matrix factorisation recommender system
- Assignment 3: Implement a content based recommender
- Assignment 4: Prepare previous algorithms as modules and benchmark
- Project:[SS 2020]: Twitter RecSys Challenge. Can be partially combined with Data-intensive Computing class.
Depending on previous experiences with matrix calculations and python around 2-6 hours per assignment when working alone.
Dauer der Zeugnisausstellung
Verbesserungsvorschläge / Kritik
Slides are partly quite minimalistic. A more exhaustive and detailed script would be very much appreciated.