TU Wien:Recommender Systems VU (Neidhardt)

Daten

Vortragende	Thomas Elmar Kolb• Julia Neidhardt
ECTS	3,0
Letzte Abhaltung	2024S
Sprache	English
Mattermost	recommender-systems • Register • Mattermost-Infos
Links	tiss:194035, eLearning

Zuordnungen

Masterstudium Data Science	Modul MLS/CO - Machine Learning and Statistics - Core
Masterstudium Business Informatics	Modul EE/COR - Enterprise Engineering Core (Gebundenes Wahlfach)

Inhalt

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Ablauf

• 4 short exercises on JupyterHub (20 points)
• 1 group project with 5-8 persons (30 points)
• 1 exam, 60 minutes (50 points)

Benötigte/Empfehlenswerte Vorkenntnisse

• Exercises: Basic python programming
• Project: Basic python programming, Neural Network and Git knowledge very useful

Vortrag

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Übungen

2024S:

Individual assignments 1 to 4 proceeded as before. This time, the group assignment involved solving the RecSys Challenge related to recommending Danish news articles (see https://recsys.eb.dk/).

Fundamentally, the task (in groups of five) was actually interesting and enjoyable - however, nearly every circumstance prevented a thorough engagement with the content. Ultimately, this led to an incredible increase in time expenditure (60+ hours per person), which I have not previously encountered at the TU. Points of frustration included:

• A poorly documented, buggy code framework.
• Significant limitations of the environment intended for training - consistent various error messages and hardware restrictions.
• Unclear specifications regarding submission and execution.

Compiling a functioning submission, including data, took an exceedingly long time. While well-intentioned, the exercise turned out to be excessive! Hopefully, this will not occur again. Additionally, the Danish newspaper involved is quite a low-quality publication similar to "Bild" in Germany - though this is, of course, irrelevant here.

To this time, grading has not be done... let's see but really difficult to do a fair rating with all hurdles and team issues. Really not a comfy situation.

Prüfung, Benotung

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Dauer der Zeugnisausstellung

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Zeitaufwand

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Unterlagen

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• Materialien

Tipps

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Highlights / Lob

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Verbesserungsvorschläge / Kritik

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Materialien

Neues Material hinzufügen

2

• 2024S recommender systems open.docx (details)

@

• @sueszli's exercise solutions (details)

E

• Exam 2022-06-28

S

• Sueszli's summary + altprüfungen (details)