TU Wien:Recommender Systems VU (Sacharidis)

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Lecturers Dimitrios Sacharidis
When summer semester
Language English
Links tiss:194035 , Mattermost-Channel
Master Data Science Pflichtmodul MLS/CO - Machine Learning and Statistics - Core
Master Business Informatics Pflichtmodul EE/COR

Mattermost: Channel ""RegisterMattermost-Infos


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

Benötigte/Empfehlenswerte Vorkenntnisse

simple matrix operations and mathematical optimisation

experience with python and the numpy and pandas modules


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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.

Prüfung, Benotung

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Dauer der Zeugnisausstellung

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Verbesserungsvorschläge / Kritik

Slides are partly quite minimalistic. A more exhaustive and detailed script would be very much appreciated.