# TU Wien:Recommender Systems VU (Sacharidis)

## Daten

Lecturers | Dimitrios Sacharidis |
---|---|

ECTS | 3 |

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 |

## Inhalt

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.

## Ablauf

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

## Vortrag

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## Übungen

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

Exam: About 4-5 open questions, to be answered in 1-2 sentences or with a mathematical formula. Those aim mostly at understanding, i.e. they ask about some of the concepts covered (e.g. similarity between items, ...) but give different examples for applying them. Additionally, for about 2-3 different topics, about 2-5 True/False statementes each. The exam is open book, i.e. all material from the lecture and self-prepared material can be used. The material was not checked. Even computers where allowed "as long as they were used for checking the slides".

### Dauer der Zeugnisausstellung

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## Zeitaufwand

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## Unterlagen

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## Tipps

noch offen

## Verbesserungsvorschläge / Kritik

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