# Recommenders ##### Summer #### Learning Methods and Evaluation Methods for Recommenders ##### (in German: Recommenders - Master level )
##### **Module-ID:** **FIN-INF-120525**
**Abbreviation** RECSYS-M **Credit Points** 9 **Semester** Summer **Term** starting at 1. **Duration** 1 Semester **Language** english **Level** Master
**Link:**[ LSF](https://lsf.ovgu.de/qislsf/rds?state=wsearchv&search=1&subdir=veranstaltung&veranstaltung.semester=20261&veranstaltung.dtxt=Recommenders+-+Master+level&P_start=0&P_anzahl=30&P.sort=veranstaltung.dtxt&_form=display "Recommenders - Master level")
**Limited accessability:**false
**Responsibility:**Myra Spiliopoulou
**Lecturer:**Myra Spiliopoulou
**Classes:**- Lecture *Recommenders* - Exercise class *Recommenders*
**Applicability in curriculum:**- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Wirtschaftsinformatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science
**Intended learning outcomes:** When successfully completing this module, the students have acquired soft skills and technical expertise, as follows: **SOFT SKILLS:** They - have learned how to critically discuss and valuate a scientific paper **TECHNICAL EXPERTISE:** They - comprehend the business context of recommenders - comprehend the different types of recommenders, their advantages and disadvantages from the business perspective and from the mining perspective and are familiar with advanced recommendation methods - have acquired advanced insights on how to critically evaluate a recommender from the mining perspective and the business perspective - understand why adaption is necessary in recommenders and comprehend the behaviour of temporal methods for recommenders and have thus acquired skills they need in order to critically appreciate off-the-shelf or state-of-the-art methods and to design workflows that encompass recommender learning, tuning and evaluation **Content:** The course consists of two blocks: - Block 1 is on Fundamentals of Recommenders and has mutiple parts: (1.1) recommender goals and workflows, (1.2) simple recommendation methods, (1.3) evaluation mechanisms, (1.4) recommender adaption on a stream - Block 2 covers advanced topics of recommenders: students read and critically discuss papers in class **Workload:** - 28 Std/hours: Vorlesung/Lecture - in presence (lecture class: 2 hours per week, 28 hours) - 28 Std/hours: Übung/Exercises - in presence (exercise class: 2 hours per week, 28 hours) - 214 Stunden: Individual working time on - Preparation for lectures and exercise classes - Reading and understanding the articles provided - Discussion of the articles in class
**Pre-examination requirements:****Type of examination:****Teaching method / lecture hours per week (SWS):**
A minimum number of points must be achieved during the classes Schriftliche Prüfung (Klausur) - Vorlesung (2 SWS) - Lecture class (2 hours-per-week) - Übung (2 SWS) - Exercise class (2 hours-per-week)
**Prerequisites according to examination regulations:****Recommended prerequisites:**
keine - Data Mining I - Machine Learning - Data Mining II - Introduction to Deep Learning - Advanced English skills - Reading of scientific papers, as acquired eg in a bachelor seminar
**Media:****Literature:**
- Literature for Block 1: selected chapters from the 'Recommender Systems Handbook' in the editions: - Recommender Systems Handbook by Francesco Ricci, Lior Rokach and Bracha Shapira (editors), SPRINGER (2015), 2nd edition, [https://doi.org/10.1007/978-1-4899-7637-6](https://doi.org/10.1007/978-1-4899-7637-6) - Recommender Systems Handbook by Francesco Ricci, Lior Rokach and Bracha Shapira (editors), SPRINGER (2022), 3rd edition, [https://doi.org/10.1007/978-1-0716-2197-4](https://doi.org/10.1007/978-1-0716-2197-4) - Further cited literature, including scientific articles and use cases are cited at the beginning of each unit. - Literature for Block 2: the papers for the discussions in class will be announced during the course
**Comments:**